second half

.idea/TalkSHOW.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/TalkSHOW.iml" filepath="$PROJECT_DIR$/.idea/TalkSHOW.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.idea/workspace.xml

@@ -0,0 +1,55 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="210ec380-7aeb-4dbc-977d-b398d2f30cdf" name="Changes" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+  </component>
+  <component name="GitHubPullRequestSearchHistory"><![CDATA[{
+  "lastFilter": {
+    "state": "OPEN",
+    "assignee": "khaitha"
+  }
+}]]></component>
+  <component name="ProjectColorInfo"><![CDATA[{
+  "associatedIndex": 5
+}]]></component>
+  <component name="ProjectId" id="2nX5jW7aFzy8GxYLTCwAZXC0ETa" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent"><![CDATA[{
+  "keyToString": {
+    "RunOnceActivity.ShowReadmeOnStart": "true",
+    "git-widget-placeholder": "main",
+    "nodejs_package_manager_path": "npm",
+    "vue.rearranger.settings.migration": "true"
+  }
+}]]></component>
+  <component name="SharedIndexes">
+    <attachedChunks>
+      <set>
+        <option value="bundled-js-predefined-d6986cc7102b-5c90d61e3bab-JavaScript-PY-242.23339.19" />
+        <option value="bundled-python-sdk-0029f7779945-399fe30bd8c1-com.jetbrains.pycharm.pro.sharedIndexes.bundled-PY-242.23339.19" />
+      </set>
+    </attachedChunks>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="210ec380-7aeb-4dbc-977d-b398d2f30cdf" name="Changes" comment="" />
+      <created>1729106717261</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1729106717261</updated>
+      <workItem from="1729106719428" duration="5000" />
+    </task>
+    <servers />
+  </component>
+</project>

config/LS3DCG.json

@@ -0,0 +1,64 @@
+{
+  "config_root_path": "/is/cluster/scratch/hyi/ExpressiveBody/SMPLifyX4/scripts",
+  "dataset_load_mode": "pickle",
+  "store_file_path": "store.pkl",
+  "smplx_npz_path": "visualise/smplx_model/SMPLX_NEUTRAL_2020.npz",
+  "extra_joint_path": "visualise/smplx_model/smplx_extra_joints.yaml",
+  "j14_regressor_path": "visualise/smplx_model/SMPLX_to_J14.pkl",
+  "param": {
+    "w_j": 1,
+    "w_b": 1,
+    "w_h": 1
+  },
+  "Data": {
+    "data_root": "../ExpressiveWholeBodyDatasetv1.0/",
+    "pklname": "_3d_mfcc.pkl",
+    "whole_video": false,
+    "pose": {
+      "normalization": false,
+      "convert_to_6d": false,
+      "norm_method": "all",
+      "augmentation": false,
+      "generate_length": 88,
+      "pre_pose_length": 0,
+      "pose_dim": 99,
+      "expression": true
+    },
+    "aud": {
+      "feat_method": "mfcc",
+      "aud_feat_dim": 64,
+      "aud_feat_win_size": null,
+      "context_info": false
+    }
+  },
+  "Model": {
+    "model_type": "body",
+    "model_name": "s2g_LS3DCG",
+    "code_num": 2048,
+    "AudioOpt": "Adam",
+    "encoder_choice": "mfcc",
+    "gan": false
+  },
+  "DataLoader": {
+    "batch_size": 128,
+    "num_workers": 0
+  },
+  "Train": {
+    "epochs": 100,
+    "max_gradient_norm": 5,
+    "learning_rate": {
+      "generator_learning_rate": 1e-4,
+      "discriminator_learning_rate": 1e-4
+    },
+    "weights": {
+      "keypoint_loss_weight": 1.0,
+      "gan_loss_weight": 1.0
+    }
+  },
+  "Log": {
+    "save_every": 50,
+    "print_every": 200,
+    "name": "LS3DCG"
+  }
+}
+  

config/body_pixel.json

@@ -0,0 +1,63 @@
+{
+  "config_root_path": "/is/cluster/scratch/hyi/ExpressiveBody/SMPLifyX4/scripts",
+  "dataset_load_mode": "json",
+  "store_file_path": "store.pkl",
+  "smplx_npz_path": "visualise/smplx_model/SMPLX_NEUTRAL_2020.npz",
+  "extra_joint_path": "visualise/smplx_model/smplx_extra_joints.yaml",
+  "j14_regressor_path": "visualise/smplx_model/SMPLX_to_J14.pkl",
+  "param": {
+    "w_j": 1,
+    "w_b": 1,
+    "w_h": 1
+  },
+  "Data": {
+    "data_root": "../ExpressiveWholeBodyDatasetv1.0/",
+    "pklname": "_3d_mfcc.pkl",
+    "whole_video": false,
+    "pose": {
+      "normalization": false,
+      "convert_to_6d": false,
+      "norm_method": "all",
+      "augmentation": false,
+      "generate_length": 88,
+      "pre_pose_length": 0,
+      "pose_dim": 99,
+      "expression": true
+    },
+    "aud": {
+      "feat_method": "mfcc",
+      "aud_feat_dim": 64,
+      "aud_feat_win_size": null,
+      "context_info": false
+    }
+  },
+  "Model": {
+    "model_type": "body",
+    "model_name": "s2g_body_pixel",
+    "composition": true,
+    "code_num": 2048,
+    "bh_model": true,
+    "AudioOpt": "Adam",
+    "encoder_choice": "mfcc",
+    "gan": false,
+    "vq_path": "./experiments/2022-10-31-smplx_S2G-body-vq-3d/ckpt-99.pth"
+  },
+  "DataLoader": {
+    "batch_size": 128,
+    "num_workers": 0
+  },
+  "Train": {
+    "epochs": 100,
+    "max_gradient_norm": 5,
+    "learning_rate": {
+      "generator_learning_rate": 1e-4,
+      "discriminator_learning_rate": 1e-4
+    }
+  },
+  "Log": {
+    "save_every": 50,
+    "print_every": 200,
+    "name": "body-pixel2"
+  }
+}
+  

config/body_vq.json

@@ -0,0 +1,62 @@
+{
+  "config_root_path": "/is/cluster/scratch/hyi/ExpressiveBody/SMPLifyX4/scripts",
+  "dataset_load_mode": "json",
+  "store_file_path": "store.pkl",
+  "smplx_npz_path": "visualise/smplx_model/SMPLX_NEUTRAL_2020.npz",
+  "extra_joint_path": "visualise/smplx_model/smplx_extra_joints.yaml",
+  "j14_regressor_path": "visualise/smplx_model/SMPLX_to_J14.pkl",
+  "param": {
+    "w_j": 1,
+    "w_b": 1,
+    "w_h": 1
+  },
+  "Data": {
+    "data_root": "../ExpressiveWholeBodyDatasetv1.0/",
+    "pklname": "_3d_mfcc.pkl",
+    "whole_video": false,
+    "pose": {
+      "normalization": false,
+      "convert_to_6d": false,
+      "norm_method": "all",
+      "augmentation": false,
+      "generate_length": 88,
+      "pre_pose_length": 0,
+      "pose_dim": 99,
+      "expression": true
+    },
+    "aud": {
+      "feat_method": "mfcc",
+      "aud_feat_dim": 64,
+      "aud_feat_win_size": null,
+      "context_info": false
+    }
+  },
+  "Model": {
+    "model_type": "body",
+    "model_name": "s2g_body_vq",
+    "composition": true,
+    "code_num": 2048,
+    "bh_model": true,
+    "AudioOpt": "Adam",
+    "encoder_choice": "mfcc",
+    "gan": false
+  },
+  "DataLoader": {
+    "batch_size": 128,
+    "num_workers": 0
+  },
+  "Train": {
+    "epochs": 100,
+    "max_gradient_norm": 5,
+    "learning_rate": {
+      "generator_learning_rate": 1e-4,
+      "discriminator_learning_rate": 1e-4
+    }
+  },
+  "Log": {
+    "save_every": 50,
+    "print_every": 200,
+    "name": "body-vq"
+  }
+}
+  

config/face.json

@@ -0,0 +1,59 @@
+{
+  "config_root_path": "/is/cluster/scratch/hyi/ExpressiveBody/SMPLifyX4/scripts",
+  "dataset_load_mode": "json",
+  "store_file_path": "store.pkl",
+  "smplx_npz_path": "visualise/smplx_model/SMPLX_NEUTRAL_2020.npz",
+  "extra_joint_path": "visualise/smplx_model/smplx_extra_joints.yaml",
+  "j14_regressor_path": "visualise/smplx_model/SMPLX_to_J14.pkl",
+  "param": {
+    "w_j": 1,
+    "w_b": 1,
+    "w_h": 1
+  },
+  "Data": {
+    "data_root": "../ExpressiveWholeBodyDatasetv1.0/",
+    "pklname": "_3d_wv2.pkl",
+    "whole_video": true,
+    "pose": {
+      "normalization": false,
+      "convert_to_6d": false,
+      "norm_method": "all",
+      "augmentation": false,
+      "generate_length": 88,
+      "pre_pose_length": 0,
+      "pose_dim": 99,
+      "expression": true
+    },
+    "aud": {
+      "feat_method": "mfcc",
+      "aud_feat_dim": 64,
+      "aud_feat_win_size": null,
+      "context_info": false
+    }
+  },
+  "Model": {
+    "model_type": "face",
+    "model_name": "s2g_face",
+    "AudioOpt": "SGD",
+    "encoder_choice": "faceformer",
+    "gan": false
+  },
+  "DataLoader": {
+    "batch_size": 1,
+    "num_workers": 0
+  },
+  "Train": {
+    "epochs": 100,
+    "max_gradient_norm": 5,
+    "learning_rate": {
+      "generator_learning_rate": 1e-4,
+      "discriminator_learning_rate": 1e-4
+    }
+  },
+  "Log": {
+    "save_every": 50,
+    "print_every": 1000,
+    "name": "face"
+  }
+}
+  

data_utils/__init__.py

@@ -0,0 +1,3 @@
+# from .dataloader_csv import MultiVidData as csv_data
+from .dataloader_torch import MultiVidData as torch_data
+from .utils import get_melspec, get_mfcc, get_mfcc_old, get_mfcc_psf, get_mfcc_psf_min, get_mfcc_ta

data_utils/apply_split.py

@@ -0,0 +1,51 @@
+import os
+from tqdm import tqdm
+import pickle
+import shutil
+
+speakers = ['seth', 'oliver', 'conan', 'chemistry']
+source_data_root = "../expressive_body-V0.7"
+data_root = "D:/Downloads/SHOW_dataset_v1.0/ExpressiveWholeBodyDatasetReleaseV1.0"
+
+f_read = open('split_more_than_2s.pkl', 'rb')
+f_save = open('none.pkl', 'wb')
+data_split = pickle.load(f_read)
+none_split = []
+
+train = val = test = 0
+
+for speaker_name in speakers:
+    speaker_root = os.path.join(data_root, speaker_name)
+
+    videos = [v for v in data_split[speaker_name]]
+
+    for vid in tqdm(videos, desc="Processing training data of {}......".format(speaker_name)):
+        for split in data_split[speaker_name][vid]:
+            for seq in data_split[speaker_name][vid][split]:
+
+                seq = seq.replace('\\', '/')
+                old_file_path = os.path.join(data_root, speaker_name, vid, seq.split('/')[-1])
+                old_file_path = old_file_path.replace('\\', '/')
+                new_file_path = seq.replace(source_data_root.split('/')[-1], data_root.split('/')[-1])
+                try:
+                    shutil.move(old_file_path, new_file_path)
+                    if split == 'train':
+                        train = train + 1
+                    elif split == 'test':
+                        test = test + 1
+                    elif split == 'val':
+                        val = val + 1
+                except FileNotFoundError:
+                    none_split.append(old_file_path)
+                    print(f"The file {old_file_path} does not exists.")
+                except shutil.Error:
+                    none_split.append(old_file_path)
+                    print(f"The file {old_file_path} does not exists.")
+
+print(none_split.__len__())
+pickle.dump(none_split, f_save)
+f_save.close()
+
+print(train, val, test)
+
+

data_utils/axis2matrix.py

@@ -0,0 +1,29 @@
+import numpy as np
+import math
+import scipy.linalg as linalg
+
+
+def rotate_mat(axis, radian):
+
+    a = np.cross(np.eye(3), axis / linalg.norm(axis) * radian)
+
+    rot_matrix = linalg.expm(a)
+
+    return rot_matrix
+
+def aaa2mat(axis, sin, cos):
+    i = np.eye(3)
+    nnt = np.dot(axis.T, axis)
+    s = np.asarray([[0, -axis[0,2], axis[0,1]],
+                    [axis[0,2], 0, -axis[0,0]],
+                    [-axis[0,1], axis[0,0], 0]])
+    r = cos * i + (1-cos)*nnt +sin * s
+    return r
+
+rand_axis = np.asarray([[1,0,0]])
+#旋转角度
+r = math.pi/2
+#返回旋转矩阵
+rot_matrix = rotate_mat(rand_axis, r)
+r2 = aaa2mat(rand_axis, np.sin(r), np.cos(r))
+print(rot_matrix)

data_utils/dataloader_torch.py

@@ -0,0 +1,279 @@
+import sys
+import os
+sys.path.append(os.getcwd())
+import os
+from tqdm import tqdm
+from data_utils.utils import *
+import torch.utils.data as data
+from data_utils.mesh_dataset import SmplxDataset
+from transformers import Wav2Vec2Processor
+
+
+class MultiVidData():
+    def __init__(self, 
+                data_root, 
+                speakers, 
+                split='train', 
+                limbscaling=False, 
+                normalization=False,
+                norm_method='new',
+                split_trans_zero=False,
+                num_frames=25,
+                num_pre_frames=25,
+                num_generate_length=None,
+                aud_feat_win_size=None,
+                aud_feat_dim=64,
+                feat_method='mel_spec',
+                context_info=False,
+                smplx=False,
+                audio_sr=16000,
+                convert_to_6d=False,
+                expression=False,
+                config=None
+                ):
+        self.data_root = data_root
+        self.speakers = speakers
+        self.split = split
+        if split == 'pre':
+            self.split = 'train'
+        self.norm_method=norm_method
+        self.normalization = normalization
+        self.limbscaling = limbscaling
+        self.convert_to_6d = convert_to_6d
+        self.num_frames=num_frames
+        self.num_pre_frames=num_pre_frames
+        if num_generate_length is None:
+            self.num_generate_length = num_frames
+        else:
+            self.num_generate_length = num_generate_length
+        self.split_trans_zero=split_trans_zero
+
+        dataset = SmplxDataset
+        
+        if self.split_trans_zero:
+            self.trans_dataset_list = []
+            self.zero_dataset_list = []
+        else:
+            self.all_dataset_list = []
+        self.dataset={}
+        self.complete_data=[]
+        self.config=config
+        load_mode=self.config.dataset_load_mode
+        
+        ######################load with pickle file
+        if load_mode=='pickle':
+            import pickle
+            import subprocess
+            
+            # store_file_path='/tmp/store.pkl'
+            # cp /is/cluster/scratch/hyi/ExpressiveBody/SMPLifyX4/scripts/store.pkl /tmp/store.pkl
+            # subprocess.run(f'cp /is/cluster/scratch/hyi/ExpressiveBody/SMPLifyX4/scripts/store.pkl {store_file_path}',shell=True)
+            
+            # f = open(self.config.store_file_path, 'rb+')
+            f = open(self.split+config.Data.pklname, 'rb+')
+            self.dataset=pickle.load(f)
+            f.close()
+            for key in self.dataset:
+                self.complete_data.append(self.dataset[key].complete_data)
+        ######################load with pickle file
+                
+        ######################load with a csv file
+        elif load_mode=='csv':
+
+            # 这里从我的一个code文件夹导入的，后续再完善进来
+            try:
+                sys.path.append(self.config.config_root_path)
+                from config import config_path
+                from csv_parser import csv_parse
+                
+            except ImportError as e:
+                print(f'err: {e}')
+                raise ImportError('config root path error...')
+
+
+            for speaker_name in self.speakers:
+                # df_intervals=pd.read_csv(self.config.voca_csv_file_path)
+                df_intervals=None
+                df_intervals=df_intervals[df_intervals['speaker']==speaker_name]
+                df_intervals = df_intervals[df_intervals['dataset'] == self.split]
+
+                print(f'speaker {speaker_name} train interval length: {len(df_intervals)}')
+                for iter_index, (_, interval) in tqdm(
+                        (enumerate(df_intervals.iterrows())),desc=f'load {speaker_name}'
+                ):
+                    
+                    (
+                        interval_index,
+                        interval_speaker,
+                        interval_video_fn,
+                        interval_id,
+                        
+                        start_time,
+                        end_time,
+                        duration_time,
+                        start_time_10,
+                        over_flow_flag,
+                        short_dur_flag,
+                        
+                        big_video_dir,
+                        small_video_dir_name,
+                        speaker_video_path,
+                        
+                        voca_basename,
+                        json_basename,
+                        wav_basename,
+                        voca_top_clip_path,
+                        voca_json_clip_path,
+                        voca_wav_clip_path,
+                        
+                        audio_output_fn,
+                        image_output_path,
+                        pifpaf_output_path,
+                        mp_output_path,
+                        op_output_path,
+                        deca_output_path,
+                        pixie_output_path,
+                        cam_output_path, 
+                        ours_output_path,
+                        merge_output_path,
+                        multi_output_path,
+                        gt_output_path,
+                        ours_images_path,
+                        pkl_fil_path,
+                    )=csv_parse(interval)
+                    
+                    if not os.path.exists(pkl_fil_path) or not os.path.exists(audio_output_fn):
+                        continue
+
+                    key=f'{interval_video_fn}/{small_video_dir_name}'
+                    self.dataset[key] = dataset(
+                        data_root=pkl_fil_path,
+                        speaker=speaker_name,
+                        audio_fn=audio_output_fn,
+                        audio_sr=audio_sr,
+                        fps=num_frames,
+                        feat_method=feat_method,
+                        audio_feat_dim=aud_feat_dim,
+                        train=(self.split == 'train'),
+                        load_all=True,
+                        split_trans_zero=self.split_trans_zero,
+                        limbscaling=self.limbscaling,
+                        num_frames=self.num_frames,
+                        num_pre_frames=self.num_pre_frames,
+                        num_generate_length=self.num_generate_length,
+                        audio_feat_win_size=aud_feat_win_size,
+                        context_info=context_info,
+                        convert_to_6d=convert_to_6d,
+                        expression=expression,
+                        config=self.config
+                    )
+                    self.complete_data.append(self.dataset[key].complete_data)
+        ######################load with a csv file
+                
+        ######################origin load method
+        elif load_mode=='json':
+            
+            # if self.split == 'train':
+            #     import pickle
+            #     f = open('store.pkl', 'rb+')
+            #     self.dataset=pickle.load(f)
+            #     f.close()
+            #     for key in self.dataset:
+            #         self.complete_data.append(self.dataset[key].complete_data)
+            # else:https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav
+            # if config.Model.model_type == 'face':
+            am = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-phoneme")
+            am_sr = 16000
+            # else:
+            #     am, am_sr = None, None
+            for speaker_name in self.speakers:
+                speaker_root = os.path.join(self.data_root, speaker_name)
+
+                videos=[v for v in os.listdir(speaker_root) ]
+                print(videos)
+
+                haode = huaide = 0
+
+                for vid in tqdm(videos, desc="Processing training data of {}......".format(speaker_name)):
+                    source_vid=vid
+                    # vid_pth=os.path.join(speaker_root, source_vid, 'images/half', self.split)
+                    vid_pth = os.path.join(speaker_root, source_vid, self.split)
+                    if smplx == 'pose':
+                        seqs = [s for s in os.listdir(vid_pth) if (s.startswith('clip'))]
+                    else:
+                        try:
+                            seqs = [s for s in os.listdir(vid_pth)]
+                        except:
+                            continue
+
+                    for s in seqs:
+                        seq_root=os.path.join(vid_pth, s)
+                        key = seq_root # correspond to clip******
+                        audio_fname = os.path.join(speaker_root, source_vid, self.split, s, '%s.wav' % (s))
+                        motion_fname = os.path.join(speaker_root, source_vid, self.split, s, '%s.pkl' % (s))
+                        if not os.path.isfile(audio_fname) or not os.path.isfile(motion_fname):
+                            huaide = huaide + 1
+                            continue
+
+                        self.dataset[key]=dataset(
+                            data_root=seq_root,
+                            speaker=speaker_name,
+                            motion_fn=motion_fname,
+                            audio_fn=audio_fname,
+                            audio_sr=audio_sr,
+                            fps=num_frames,
+                            feat_method=feat_method,
+                            audio_feat_dim=aud_feat_dim,
+                            train=(self.split=='train'),
+                            load_all=True,
+                            split_trans_zero=self.split_trans_zero,
+                            limbscaling=self.limbscaling,
+                            num_frames=self.num_frames,
+                            num_pre_frames=self.num_pre_frames,
+                            num_generate_length=self.num_generate_length,
+                            audio_feat_win_size=aud_feat_win_size,
+                            context_info=context_info,
+                            convert_to_6d=convert_to_6d,
+                            expression=expression,
+                            config=self.config,
+                            am=am,
+                            am_sr=am_sr,
+                            whole_video=config.Data.whole_video
+                        )
+                        self.complete_data.append(self.dataset[key].complete_data)
+                        haode = haode + 1
+                print("huaide:{}, haode:{}".format(huaide, haode))
+            import pickle
+
+            f = open(self.split+config.Data.pklname, 'wb')
+            pickle.dump(self.dataset, f)
+            f.close()
+        ######################origin load method
+
+        self.complete_data=np.concatenate(self.complete_data, axis=0)
+
+        # assert self.complete_data.shape[-1] == (12+21+21)*2
+        self.normalize_stats = {}
+
+        self.data_mean = None
+        self.data_std = None
+    
+    def get_dataset(self):
+        self.normalize_stats['mean'] = self.data_mean
+        self.normalize_stats['std'] = self.data_std
+
+        for key in list(self.dataset.keys()):
+            if self.dataset[key].complete_data.shape[0] < self.num_generate_length:
+                continue
+            self.dataset[key].num_generate_length = self.num_generate_length
+            self.dataset[key].get_dataset(self.normalization, self.normalize_stats, self.split)
+            self.all_dataset_list.append(self.dataset[key].all_dataset)
+        
+        if self.split_trans_zero:
+            self.trans_dataset = data.ConcatDataset(self.trans_dataset_list)
+            self.zero_dataset = data.ConcatDataset(self.zero_dataset_list)
+        else:
+            self.all_dataset = data.ConcatDataset(self.all_dataset_list)
+
+
+

data_utils/dataset_preprocess.py

@@ -0,0 +1,170 @@
+import os
+import pickle
+from tqdm import tqdm
+import shutil
+import torch
+import numpy as np
+import librosa
+import random
+
+speakers = ['seth', 'conan', 'oliver', 'chemistry']
+data_root = "../ExpressiveWholeBodyDatasetv1.0/"
+split = 'train'
+
+
+
+def split_list(full_list,shuffle=False,ratio=0.2):
+    n_total = len(full_list)
+    offset_0 = int(n_total * ratio)
+    offset_1 = int(n_total * ratio * 2)
+    if n_total==0 or offset_1<1:
+        return [],full_list
+    if shuffle:
+        random.shuffle(full_list)
+    sublist_0 = full_list[:offset_0]
+    sublist_1 = full_list[offset_0:offset_1]
+    sublist_2 = full_list[offset_1:]
+    return sublist_0, sublist_1, sublist_2
+
+
+def moveto(list, file):
+    for f in list:
+        before, after = '/'.join(f.split('/')[:-1]), f.split('/')[-1]
+        new_path = os.path.join(before, file)
+        new_path = os.path.join(new_path, after)
+        # os.makedirs(new_path)
+        # os.path.isdir(new_path)
+        # shutil.move(f, new_path)
+
+        #转移到新目录
+        shutil.copytree(f, new_path)
+        #删除原train里的文件
+        shutil.rmtree(f)
+    return None
+
+
+def read_pkl(data):
+    betas = np.array(data['betas'])
+
+    jaw_pose = np.array(data['jaw_pose'])
+    leye_pose = np.array(data['leye_pose'])
+    reye_pose = np.array(data['reye_pose'])
+    global_orient = np.array(data['global_orient']).squeeze()
+    body_pose = np.array(data['body_pose_axis'])
+    left_hand_pose = np.array(data['left_hand_pose'])
+    right_hand_pose = np.array(data['right_hand_pose'])
+
+    full_body = np.concatenate(
+        (jaw_pose, leye_pose, reye_pose, global_orient, body_pose, left_hand_pose, right_hand_pose), axis=1)
+
+    expression = np.array(data['expression'])
+    full_body = np.concatenate((full_body, expression), axis=1)
+
+    if (full_body.shape[0] < 90) or (torch.isnan(torch.from_numpy(full_body)).sum() > 0):
+        return 1
+    else:
+        return 0
+
+
+for speaker_name in speakers:
+    speaker_root = os.path.join(data_root, speaker_name)
+
+    videos = [v for v in os.listdir(speaker_root)]
+    print(videos)
+
+    haode = huaide = 0
+    total_seqs = []
+
+    for vid in tqdm(videos, desc="Processing training data of {}......".format(speaker_name)):
+    # for vid in videos:
+        source_vid = vid
+        vid_pth = os.path.join(speaker_root, source_vid)
+        # vid_pth = os.path.join(speaker_root, source_vid, 'images/half', split)
+        t = os.path.join(speaker_root, source_vid, 'test')
+        v = os.path.join(speaker_root, source_vid, 'val')
+
+        # if os.path.exists(t):
+        #     shutil.rmtree(t)
+        # if os.path.exists(v):
+        #     shutil.rmtree(v)
+        try:
+            seqs = [s for s in os.listdir(vid_pth)]
+        except:
+            continue
+        # if len(seqs) == 0:
+        #     shutil.rmtree(os.path.join(speaker_root, source_vid))
+            # None
+        for s in seqs:
+            quality = 0
+            total_seqs.append(os.path.join(vid_pth,s))
+            seq_root = os.path.join(vid_pth, s)
+            key = seq_root  # correspond to clip******
+            audio_fname = os.path.join(speaker_root, source_vid, s, '%s.wav' % (s))
+
+            # delete the data without audio or the audio file could not be read
+            if os.path.isfile(audio_fname):
+                try:
+                    audio = librosa.load(audio_fname)
+                except:
+                    # print(key)
+                    shutil.rmtree(key)
+                    huaide = huaide + 1
+                    continue
+            else:
+                huaide = huaide + 1
+                # print(key)
+                shutil.rmtree(key)
+                continue
+
+            # check motion file
+            motion_fname = os.path.join(speaker_root, source_vid, s, '%s.pkl' % (s))
+            try:
+                f = open(motion_fname, 'rb+')
+            except:
+                shutil.rmtree(key)
+                huaide = huaide + 1
+                continue
+
+            data = pickle.load(f)
+            w = read_pkl(data)
+            f.close()
+            quality = quality + w
+
+            if w == 1:
+                shutil.rmtree(key)
+                # print(key)
+                huaide = huaide + 1
+                continue
+
+            haode = haode + 1
+
+    print("huaide:{}, haode:{}, total_seqs:{}".format(huaide, haode, total_seqs.__len__()))
+
+for speaker_name in speakers:
+    speaker_root = os.path.join(data_root, speaker_name)
+
+    videos = [v for v in os.listdir(speaker_root)]
+    print(videos)
+
+    haode = huaide = 0
+    total_seqs = []
+
+    for vid in tqdm(videos, desc="Processing training data of {}......".format(speaker_name)):
+        # for vid in videos:
+        source_vid = vid
+        vid_pth = os.path.join(speaker_root, source_vid)
+        try:
+            seqs = [s for s in os.listdir(vid_pth)]
+        except:
+            continue
+        for s in seqs:
+            quality = 0
+            total_seqs.append(os.path.join(vid_pth, s))
+    print("total_seqs:{}".format(total_seqs.__len__()))
+    # split the dataset
+    test_list, val_list, train_list = split_list(total_seqs, True, 0.1)
+    print(len(test_list), len(val_list), len(train_list))
+    moveto(train_list, 'train')
+    moveto(test_list, 'test')
+    moveto(val_list, 'val')
+

data_utils/get_j.py

@@ -0,0 +1,51 @@
+import torch
+
+
+def to3d(poses, config):
+    if config.Data.pose.convert_to_6d:
+        if config.Data.pose.expression:
+            poses_exp = poses[:, -100:]
+            poses = poses[:, :-100]
+
+        poses = poses.reshape(poses.shape[0], -1, 5)
+        sin, cos = poses[:, :, 3], poses[:, :, 4]
+        pose_angle = torch.atan2(sin, cos)
+        poses = (poses[:, :, :3] * pose_angle.unsqueeze(dim=-1)).reshape(poses.shape[0], -1)
+
+        if config.Data.pose.expression:
+            poses = torch.cat([poses, poses_exp], dim=-1)
+    return poses
+
+
+def get_joint(smplx_model, betas, pred):
+    joint = smplx_model(betas=betas.repeat(pred.shape[0], 1),
+                        expression=pred[:, 165:265],
+                        jaw_pose=pred[:, 0:3],
+                        leye_pose=pred[:, 3:6],
+                        reye_pose=pred[:, 6:9],
+                        global_orient=pred[:, 9:12],
+                        body_pose=pred[:, 12:75],
+                        left_hand_pose=pred[:, 75:120],
+                        right_hand_pose=pred[:, 120:165],
+                        return_verts=True)['joints']
+    return joint
+
+
+def get_joints(smplx_model, betas, pred):
+    if len(pred.shape) == 3:
+        B = pred.shape[0]
+        x = 4 if B>= 4 else B
+        T = pred.shape[1]
+        pred = pred.reshape(-1, 265)
+        smplx_model.batch_size = L = T * x
+
+        times = pred.shape[0] // smplx_model.batch_size
+        joints = []
+        for i in range(times):
+            joints.append(get_joint(smplx_model, betas, pred[i*L:(i+1)*L]))
+        joints = torch.cat(joints, dim=0)
+        joints = joints.reshape(B, T, -1, 3)
+    else:
+        smplx_model.batch_size = pred.shape[0]
+        joints = get_joint(smplx_model, betas, pred)
+    return joints

data_utils/lower_body.py

@@ -0,0 +1,143 @@
+import numpy as np
+import torch
+
+lower_pose = torch.tensor(
+    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0747, -0.0158, -0.0152, -1.1826512813568115, 0.23866955935955048,
+     0.15146760642528534, -1.2604516744613647, -0.3160211145877838,
+     -0.1603458970785141, 1.1654603481292725, 0.0, 0.0, 1.2521806955337524, 0.041598282754421234, -0.06312154978513718,
+     0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
+lower_pose_stand = torch.tensor([
+    8.9759e-04, 7.1074e-04, -5.9163e-06, 8.9759e-04, 7.1074e-04, -5.9163e-06,
+    3.0747, -0.0158, -0.0152,
+    -3.6665e-01, -8.8455e-03, 1.6113e-01, -3.6665e-01, -8.8455e-03, 1.6113e-01,
+    -3.9716e-01, -4.0229e-02, -1.2637e-01,
+    7.9163e-01, 6.8519e-02, -1.5091e-01, 7.9163e-01, 6.8519e-02, -1.5091e-01,
+    7.8632e-01, -4.3810e-02, 1.4375e-02,
+    -1.0675e-01, 1.2635e-01, 1.6711e-02, -1.0675e-01, 1.2635e-01, 1.6711e-02, ])
+# lower_pose_stand = torch.tensor(
+#     [6.4919e-02,  3.3018e-02,  1.7485e-02,  8.9759e-04,  7.1074e-04, -5.9163e-06,
+#      3.0747, -0.0158, -0.0152,
+#      -3.3633e+00, -9.3915e-02, 3.0996e-01, -3.6665e-01, -8.8455e-03, 1.6113e-01,
+#      1.1654603481292725, 0.0, 0.0,
+#      4.4167e-01,  6.7183e-03, -3.6379e-03,  7.9163e-01,  6.8519e-02, -1.5091e-01,
+#      0.0, 0.0, 0.0,
+#      2.2910e-02, -2.4797e-02, -5.5657e-03, -1.0675e-01,  1.2635e-01,  1.6711e-02,])
+lower_body = [0, 1, 3, 4, 6, 7, 9, 10]
+count_part = [6, 9, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
+              29, 30, 31, 32, 33, 34, 35, 36, 37,
+              38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]
+fix_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
+             29,
+             35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             65, 66, 67, 68, 69, 70, 71, 72, 73, 74]
+all_index = np.ones(275)
+all_index[fix_index] = 0
+c_index = []
+i = 0
+for num in all_index:
+    if num == 1:
+        c_index.append(i)
+    i = i + 1
+c_index = np.asarray(c_index)
+
+fix_index_3d = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                21, 22, 23, 24, 25, 26,
+                30, 31, 32, 33, 34, 35,
+                45, 46, 47, 48, 49, 50]
+all_index_3d = np.ones(165)
+all_index_3d[fix_index_3d] = 0
+c_index_3d = []
+i = 0
+for num in all_index_3d:
+    if num == 1:
+        c_index_3d.append(i)
+    i = i + 1
+c_index_3d = np.asarray(c_index_3d)
+
+c_index_6d = []
+i = 0
+for num in all_index_3d:
+    if num == 1:
+        c_index_6d.append(2*i)
+        c_index_6d.append(2 * i + 1)
+    i = i + 1
+c_index_6d = np.asarray(c_index_6d)
+
+
+def part2full(input, stand=False):
+    if stand:
+        # lp = lower_pose_stand.unsqueeze(dim=0).repeat(input.shape[0], 1).to(input.device)
+        lp = torch.zeros_like(lower_pose)
+        lp[6:9] = torch.tensor([3.0747, -0.0158, -0.0152])
+        lp = lp.unsqueeze(dim=0).repeat(input.shape[0], 1).to(input.device)
+    else:
+        lp = lower_pose.unsqueeze(dim=0).repeat(input.shape[0], 1).to(input.device)
+
+    input = torch.cat([input[:, :3],
+                       lp[:, :15],
+                       input[:, 3:6],
+                       lp[:, 15:21],
+                       input[:, 6:9],
+                       lp[:, 21:27],
+                       input[:, 9:12],
+                       lp[:, 27:],
+                       input[:, 12:]]
+                      , dim=1)
+    return input
+
+
+def pred2poses(input, gt):
+    input = torch.cat([input[:, :3],
+                       gt[0:1, 3:18].repeat(input.shape[0], 1),
+                       input[:, 3:6],
+                       gt[0:1, 21:27].repeat(input.shape[0], 1),
+                       input[:, 6:9],
+                       gt[0:1, 30:36].repeat(input.shape[0], 1),
+                       input[:, 9:12],
+                       gt[0:1, 39:45].repeat(input.shape[0], 1),
+                       input[:, 12:]]
+                      , dim=1)
+    return input
+
+
+def poses2poses(input, gt):
+    input = torch.cat([input[:, :3],
+                       gt[0:1, 3:18].repeat(input.shape[0], 1),
+                       input[:, 18:21],
+                       gt[0:1, 21:27].repeat(input.shape[0], 1),
+                       input[:, 27:30],
+                       gt[0:1, 30:36].repeat(input.shape[0], 1),
+                       input[:, 36:39],
+                       gt[0:1, 39:45].repeat(input.shape[0], 1),
+                       input[:, 45:]]
+                      , dim=1)
+    return input
+
+def poses2pred(input, stand=False):
+    if stand:
+        lp = lower_pose_stand.unsqueeze(dim=0).repeat(input.shape[0], 1).to(input.device)
+        # lp = torch.zeros_like(lower_pose).unsqueeze(dim=0).repeat(input.shape[0], 1).to(input.device)
+    else:
+        lp = lower_pose.unsqueeze(dim=0).repeat(input.shape[0], 1).to(input.device)
+    input = torch.cat([input[:, :3],
+                       lp[:, :15],
+                       input[:, 18:21],
+                       lp[:, 15:21],
+                       input[:, 27:30],
+                       lp[:, 21:27],
+                       input[:, 36:39],
+                       lp[:, 27:],
+                       input[:, 45:]]
+                      , dim=1)
+    return input
+
+
+rearrange = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\
+            # ,22, 23, 24, 25, 40, 26, 41,
+            #  27, 42, 28, 43, 29, 44, 30, 45, 31, 46, 32, 47, 33, 48, 34, 49, 35, 50, 36, 51, 37, 52, 38, 53, 39, 54, 55,
+            #  57, 56, 59, 58, 60, 63, 61, 64, 62, 65, 66, 71, 67, 72, 68, 73, 69, 74, 70, 75]
+
+symmetry = [0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1]#, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            # 1, 1, 1, 1, 1, 1]

data_utils/mesh_dataset.py

@@ -0,0 +1,348 @@
+import pickle
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+import json
+from glob import glob
+from data_utils.utils import *
+import torch.utils.data as data
+from data_utils.consts import speaker_id
+from data_utils.lower_body import count_part
+import random
+from data_utils.rotation_conversion import axis_angle_to_matrix, matrix_to_rotation_6d
+
+with open('data_utils/hand_component.json') as file_obj:
+    comp = json.load(file_obj)
+    left_hand_c = np.asarray(comp['left'])
+    right_hand_c = np.asarray(comp['right'])
+
+
+def to3d(data):
+    left_hand_pose = np.einsum('bi,ij->bj', data[:, 75:87], left_hand_c[:12, :])
+    right_hand_pose = np.einsum('bi,ij->bj', data[:, 87:99], right_hand_c[:12, :])
+    data = np.concatenate((data[:, :75], left_hand_pose, right_hand_pose), axis=-1)
+    return data
+
+
+class SmplxDataset():
+    '''
+    creat a dataset for every segment and concat.
+    '''
+
+    def __init__(self,
+                 data_root,
+                 speaker,
+                 motion_fn,
+                 audio_fn,
+                 audio_sr,
+                 fps,
+                 feat_method='mel_spec',
+                 audio_feat_dim=64,
+                 audio_feat_win_size=None,
+
+                 train=True,
+                 load_all=False,
+                 split_trans_zero=False,
+                 limbscaling=False,
+                 num_frames=25,
+                 num_pre_frames=25,
+                 num_generate_length=25,
+                 context_info=False,
+                 convert_to_6d=False,
+                 expression=False,
+                 config=None,
+                 am=None,
+                 am_sr=None,
+                 whole_video=False
+                 ):
+
+        self.data_root = data_root
+        self.speaker = speaker
+
+        self.feat_method = feat_method
+        self.audio_fn = audio_fn
+        self.audio_sr = audio_sr
+        self.fps = fps
+        self.audio_feat_dim = audio_feat_dim
+        self.audio_feat_win_size = audio_feat_win_size
+        self.context_info = context_info  # for aud feat
+        self.convert_to_6d = convert_to_6d
+        self.expression = expression
+
+        self.train = train
+        self.load_all = load_all
+        self.split_trans_zero = split_trans_zero
+        self.limbscaling = limbscaling
+        self.num_frames = num_frames
+        self.num_pre_frames = num_pre_frames
+        self.num_generate_length = num_generate_length
+        # print('num_generate_length ', self.num_generate_length)
+
+        self.config = config
+        self.am_sr = am_sr
+        self.whole_video = whole_video
+        load_mode = self.config.dataset_load_mode
+
+        if load_mode == 'pickle':
+            raise NotImplementedError
+
+        elif load_mode == 'csv':
+            import pickle
+            with open(data_root, 'rb') as f:
+                u = pickle._Unpickler(f)
+                data = u.load()
+                self.data = data[0]
+            if self.load_all:
+                self._load_npz_all()
+
+        elif load_mode == 'json':
+            self.annotations = glob(data_root + '/*pkl')
+            if len(self.annotations) == 0:
+                raise FileNotFoundError(data_root + ' are empty')
+            self.annotations = sorted(self.annotations)
+            self.img_name_list = self.annotations
+
+            if self.load_all:
+                self._load_them_all(am, am_sr, motion_fn)
+
+    def _load_npz_all(self):
+        self.loaded_data = {}
+        self.complete_data = []
+        data = self.data
+        shape = data['body_pose_axis'].shape[0]
+        self.betas = data['betas']
+        self.img_name_list = []
+        for index in range(shape):
+            img_name = f'{index:6d}'
+            self.img_name_list.append(img_name)
+
+            jaw_pose = data['jaw_pose'][index]
+            leye_pose = data['leye_pose'][index]
+            reye_pose = data['reye_pose'][index]
+            global_orient = data['global_orient'][index]
+            body_pose = data['body_pose_axis'][index]
+            left_hand_pose = data['left_hand_pose'][index]
+            right_hand_pose = data['right_hand_pose'][index]
+
+            full_body = np.concatenate(
+                (jaw_pose, leye_pose, reye_pose, global_orient, body_pose, left_hand_pose, right_hand_pose))
+            assert full_body.shape[0] == 99
+            if self.convert_to_6d:
+                full_body = to3d(full_body)
+                full_body = torch.from_numpy(full_body)
+                full_body = matrix_to_rotation_6d(axis_angle_to_matrix(full_body))
+                full_body = np.asarray(full_body)
+                if self.expression:
+                    expression = data['expression'][index]
+                    full_body = np.concatenate((full_body, expression))
+                # full_body = np.concatenate((full_body, non_zero))
+            else:
+                full_body = to3d(full_body)
+                if self.expression:
+                    expression = data['expression'][index]
+                    full_body = np.concatenate((full_body, expression))
+
+            self.loaded_data[img_name] = full_body.reshape(-1)
+            self.complete_data.append(full_body.reshape(-1))
+
+        self.complete_data = np.array(self.complete_data)
+
+        if self.audio_feat_win_size is not None:
+            self.audio_feat = get_mfcc_old(self.audio_fn).transpose(1, 0)
+            # print(self.audio_feat.shape)
+        else:
+            if self.feat_method == 'mel_spec':
+                self.audio_feat = get_melspec(self.audio_fn, fps=self.fps, sr=self.audio_sr, n_mels=self.audio_feat_dim)
+            elif self.feat_method == 'mfcc':
+                self.audio_feat = get_mfcc(self.audio_fn,
+                                           smlpx=True,
+                                           sr=self.audio_sr,
+                                           n_mfcc=self.audio_feat_dim,
+                                           win_size=self.audio_feat_win_size
+                                           )
+
+    def _load_them_all(self, am, am_sr, motion_fn):
+        self.loaded_data = {}
+        self.complete_data = []
+        f = open(motion_fn, 'rb+')
+        data = pickle.load(f)
+
+        self.betas = np.array(data['betas'])
+
+        jaw_pose = np.array(data['jaw_pose'])
+        leye_pose = np.array(data['leye_pose'])
+        reye_pose = np.array(data['reye_pose'])
+        global_orient = np.array(data['global_orient']).squeeze()
+        body_pose = np.array(data['body_pose_axis'])
+        left_hand_pose = np.array(data['left_hand_pose'])
+        right_hand_pose = np.array(data['right_hand_pose'])
+
+        full_body = np.concatenate(
+            (jaw_pose, leye_pose, reye_pose, global_orient, body_pose, left_hand_pose, right_hand_pose), axis=1)
+        assert full_body.shape[1] == 99
+
+
+        if self.convert_to_6d:
+            full_body = to3d(full_body)
+            full_body = torch.from_numpy(full_body)
+            full_body = matrix_to_rotation_6d(axis_angle_to_matrix(full_body.reshape(-1, 55, 3))).reshape(-1, 330)
+            full_body = np.asarray(full_body)
+            if self.expression:
+                expression = np.array(data['expression'])
+                full_body = np.concatenate((full_body, expression), axis=1)
+
+        else:
+            full_body = to3d(full_body)
+            expression = np.array(data['expression'])
+            full_body = np.concatenate((full_body, expression), axis=1)
+
+        self.complete_data = full_body
+        self.complete_data = np.array(self.complete_data)
+
+        if self.audio_feat_win_size is not None:
+            self.audio_feat = get_mfcc_old(self.audio_fn).transpose(1, 0)
+        else:
+            # if self.feat_method == 'mel_spec':
+            #     self.audio_feat = get_melspec(self.audio_fn, fps=self.fps, sr=self.audio_sr, n_mels=self.audio_feat_dim)
+            # elif self.feat_method == 'mfcc':
+            self.audio_feat = get_mfcc_ta(self.audio_fn,
+                                          smlpx=True,
+                                          fps=30,
+                                          sr=self.audio_sr,
+                                          n_mfcc=self.audio_feat_dim,
+                                          win_size=self.audio_feat_win_size,
+                                          type=self.feat_method,
+                                          am=am,
+                                          am_sr=am_sr,
+                                          encoder_choice=self.config.Model.encoder_choice,
+                                          )
+            # with open(audio_file, 'w', encoding='utf-8') as file:
+            #     file.write(json.dumps(self.audio_feat.__array__().tolist(), indent=0, ensure_ascii=False))
+
+    def get_dataset(self, normalization=False, normalize_stats=None, split='train'):
+
+        class __Worker__(data.Dataset):
+            def __init__(child, index_list, normalization, normalize_stats, split='train') -> None:
+                super().__init__()
+                child.index_list = index_list
+                child.normalization = normalization
+                child.normalize_stats = normalize_stats
+                child.split = split
+
+            def __getitem__(child, index):
+                num_generate_length = self.num_generate_length
+                num_pre_frames = self.num_pre_frames
+                seq_len = num_generate_length + num_pre_frames
+                # print(num_generate_length)
+
+                index = child.index_list[index]
+                index_new = index + random.randrange(0, 5, 3)
+                if index_new + seq_len > self.complete_data.shape[0]:
+                    index_new = index
+                index = index_new
+
+                if child.split in ['val', 'pre', 'test'] or self.whole_video:
+                    index = 0
+                    seq_len = self.complete_data.shape[0]
+                seq_data = []
+                assert index + seq_len <= self.complete_data.shape[0]
+                # print(seq_len)
+                seq_data = self.complete_data[index:(index + seq_len), :]
+                seq_data = np.array(seq_data)
+
+                '''
+                audio feature，
+                '''
+                if not self.context_info:
+                    if not self.whole_video:
+                        audio_feat = self.audio_feat[index:index + seq_len, ...]
+                        if audio_feat.shape[0] < seq_len:
+                            audio_feat = np.pad(audio_feat, [[0, seq_len - audio_feat.shape[0]], [0, 0]],
+                                                mode='reflect')
+
+                        assert audio_feat.shape[0] == seq_len and audio_feat.shape[1] == self.audio_feat_dim
+                    else:
+                        audio_feat = self.audio_feat
+
+                else:  # including feature and history
+                    if self.audio_feat_win_size is None:
+                        audio_feat = self.audio_feat[index:index + seq_len + num_pre_frames, ...]
+                        if audio_feat.shape[0] < seq_len + num_pre_frames:
+                            audio_feat = np.pad(audio_feat,
+                                                [[0, seq_len + self.num_frames - audio_feat.shape[0]], [0, 0]],
+                                                mode='constant')
+
+                        assert audio_feat.shape[0] == self.num_frames + seq_len and audio_feat.shape[
+                            1] == self.audio_feat_dim
+
+                if child.normalization:
+                    data_mean = child.normalize_stats['mean'].reshape(1, -1)
+                    data_std = child.normalize_stats['std'].reshape(1, -1)
+                    seq_data[:, :330] = (seq_data[:, :330] - data_mean) / data_std
+                if child.split in['train', 'test']:
+                    if self.convert_to_6d:
+                        if self.expression:
+                            data_sample = {
+                                'poses': seq_data[:, :330].astype(np.float).transpose(1, 0),
+                                'expression': seq_data[:, 330:].astype(np.float).transpose(1, 0),
+                                # 'nzero': seq_data[:, 375:].astype(np.float).transpose(1, 0),
+                                'aud_feat': audio_feat.astype(np.float).transpose(1, 0),
+                                'speaker': speaker_id[self.speaker],
+                                'betas': self.betas,
+                                'aud_file': self.audio_fn,
+                            }
+                        else:
+                            data_sample = {
+                                'poses': seq_data[:, :330].astype(np.float).transpose(1, 0),
+                                'nzero': seq_data[:, 330:].astype(np.float).transpose(1, 0),
+                                'aud_feat': audio_feat.astype(np.float).transpose(1, 0),
+                                'speaker': speaker_id[self.speaker],
+                                'betas': self.betas
+                            }
+                    else:
+                        if self.expression:
+                            data_sample = {
+                                'poses': seq_data[:, :165].astype(np.float).transpose(1, 0),
+                                'expression': seq_data[:, 165:].astype(np.float).transpose(1, 0),
+                                'aud_feat': audio_feat.astype(np.float).transpose(1, 0),
+                                # 'wv2_feat': wv2_feat.astype(np.float).transpose(1, 0),
+                                'speaker': speaker_id[self.speaker],
+                                'aud_file': self.audio_fn,
+                                'betas': self.betas
+                            }
+                        else:
+                            data_sample = {
+                                'poses': seq_data.astype(np.float).transpose(1, 0),
+                                'aud_feat': audio_feat.astype(np.float).transpose(1, 0),
+                                'speaker': speaker_id[self.speaker],
+                                'betas': self.betas
+                            }
+                    return data_sample
+                else:
+                    data_sample = {
+                        'poses': seq_data[:, :330].astype(np.float).transpose(1, 0),
+                        'expression': seq_data[:, 330:].astype(np.float).transpose(1, 0),
+                        # 'nzero': seq_data[:, 325:].astype(np.float).transpose(1, 0),
+                        'aud_feat': audio_feat.astype(np.float).transpose(1, 0),
+                        'aud_file': self.audio_fn,
+                        'speaker': speaker_id[self.speaker],
+                        'betas': self.betas
+                    }
+                    return data_sample
+            def __len__(child):
+                return len(child.index_list)
+
+        if split == 'train':
+            index_list = list(
+                range(0, min(self.complete_data.shape[0], self.audio_feat.shape[0]) - self.num_generate_length - self.num_pre_frames,
+                      6))
+        elif split in ['val', 'test']:
+            index_list = list([0])
+        if self.whole_video:
+            index_list = list([0])
+        self.all_dataset = __Worker__(index_list, normalization, normalize_stats, split)
+
+    def __len__(self):
+        return len(self.img_name_list)

data_utils/rotation_conversion.py

@@ -0,0 +1,551 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+# Check PYTORCH3D_LICENCE before use
+
+import functools
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+
+"""
+The transformation matrices returned from the functions in this file assume
+the points on which the transformation will be applied are column vectors.
+i.e. the R matrix is structured as
+
+    R = [
+            [Rxx, Rxy, Rxz],
+            [Ryx, Ryy, Ryz],
+            [Rzx, Rzy, Rzz],
+        ]  # (3, 3)
+
+This matrix can be applied to column vectors by post multiplication
+by the points e.g.
+
+    points = [[0], [1], [2]]  # (3 x 1) xyz coordinates of a point
+    transformed_points = R * points
+
+To apply the same matrix to points which are row vectors, the R matrix
+can be transposed and pre multiplied by the points:
+
+e.g.
+    points = [[0, 1, 2]]  # (1 x 3) xyz coordinates of a point
+    transformed_points = points * R.transpose(1, 0)
+"""
+
+
+def quaternion_to_matrix(quaternions):
+    """
+    Convert rotations given as quaternions to rotation matrices.
+
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    r, i, j, k = torch.unbind(quaternions, -1)
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def _copysign(a, b):
+    """
+    Return a tensor where each element has the absolute value taken from the,
+    corresponding element of a, with sign taken from the corresponding
+    element of b. This is like the standard copysign floating-point operation,
+    but is not careful about negative 0 and NaN.
+
+    Args:
+        a: source tensor.
+        b: tensor whose signs will be used, of the same shape as a.
+
+    Returns:
+        Tensor of the same shape as a with the signs of b.
+    """
+    signs_differ = (a < 0) != (b < 0)
+    return torch.where(signs_differ, -a, a)
+
+
+def _sqrt_positive_part(x):
+    """
+    Returns torch.sqrt(torch.max(0, x))
+    but with a zero subgradient where x is 0.
+    """
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    ret[positive_mask] = torch.sqrt(x[positive_mask])
+    return ret
+
+
+def matrix_to_quaternion(matrix):
+    """
+    Convert rotations given as rotation matrices to quaternions.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
+    m00 = matrix[..., 0, 0]
+    m11 = matrix[..., 1, 1]
+    m22 = matrix[..., 2, 2]
+    o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22)
+    x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22)
+    y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22)
+    z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22)
+    o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2])
+    o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0])
+    o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1])
+    return torch.stack((o0, o1, o2, o3), -1)
+
+
+def _axis_angle_rotation(axis: str, angle):
+    """
+    Return the rotation matrices for one of the rotations about an axis
+    of which Euler angles describe, for each value of the angle given.
+
+    Args:
+        axis: Axis label "X" or "Y or "Z".
+        angle: any shape tensor of Euler angles in radians
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+
+    cos = torch.cos(angle)
+    sin = torch.sin(angle)
+    one = torch.ones_like(angle)
+    zero = torch.zeros_like(angle)
+
+    if axis == "X":
+        R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
+    if axis == "Y":
+        R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
+    if axis == "Z":
+        R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
+
+    return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
+
+
+def euler_angles_to_matrix(euler_angles, convention: str):
+    """
+    Convert rotations given as Euler angles in radians to rotation matrices.
+
+    Args:
+        euler_angles: Euler angles in radians as tensor of shape (..., 3).
+        convention: Convention string of three uppercase letters from
+            {"X", "Y", and "Z"}.
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
+        raise ValueError("Invalid input euler angles.")
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
+    return functools.reduce(torch.matmul, matrices)
+
+
+def _angle_from_tan(
+    axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
+):
+    """
+    Extract the first or third Euler angle from the two members of
+    the matrix which are positive constant times its sine and cosine.
+
+    Args:
+        axis: Axis label "X" or "Y or "Z" for the angle we are finding.
+        other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
+            convention.
+        data: Rotation matrices as tensor of shape (..., 3, 3).
+        horizontal: Whether we are looking for the angle for the third axis,
+            which means the relevant entries are in the same row of the
+            rotation matrix. If not, they are in the same column.
+        tait_bryan: Whether the first and third axes in the convention differ.
+
+    Returns:
+        Euler Angles in radians for each matrix in data as a tensor
+        of shape (...).
+    """
+
+    i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
+    if horizontal:
+        i2, i1 = i1, i2
+    even = (axis + other_axis) in ["XY", "YZ", "ZX"]
+    if horizontal == even:
+        return torch.atan2(data[..., i1], data[..., i2])
+    if tait_bryan:
+        return torch.atan2(-data[..., i2], data[..., i1])
+    return torch.atan2(data[..., i2], -data[..., i1])
+
+
+def _index_from_letter(letter: str):
+    if letter == "X":
+        return 0
+    if letter == "Y":
+        return 1
+    if letter == "Z":
+        return 2
+
+
+def matrix_to_euler_angles(matrix, convention: str):
+    """
+    Convert rotations given as rotation matrices to Euler angles in radians.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+        convention: Convention string of three uppercase letters.
+
+    Returns:
+        Euler angles in radians as tensor of shape (..., 3).
+    """
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
+    i0 = _index_from_letter(convention[0])
+    i2 = _index_from_letter(convention[2])
+    tait_bryan = i0 != i2
+    if tait_bryan:
+        central_angle = torch.asin(
+            matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
+        )
+    else:
+        central_angle = torch.acos(matrix[..., i0, i0])
+
+    o = (
+        _angle_from_tan(
+            convention[0], convention[1], matrix[..., i2], False, tait_bryan
+        ),
+        central_angle,
+        _angle_from_tan(
+            convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
+        ),
+    )
+    return torch.stack(o, -1)
+
+
+def random_quaternions(
+    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate random quaternions representing rotations,
+    i.e. versors with nonnegative real part.
+
+    Args:
+        n: Number of quaternions in a batch to return.
+        dtype: Type to return.
+        device: Desired device of returned tensor. Default:
+            uses the current device for the default tensor type.
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set.
+
+    Returns:
+        Quaternions as tensor of shape (N, 4).
+    """
+    o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad)
+    s = (o * o).sum(1)
+    o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
+    return o
+
+
+def random_rotations(
+    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate random rotations as 3x3 rotation matrices.
+
+    Args:
+        n: Number of rotation matrices in a batch to return.
+        dtype: Type to return.
+        device: Device of returned tensor. Default: if None,
+            uses the current device for the default tensor type.
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set.
+
+    Returns:
+        Rotation matrices as tensor of shape (n, 3, 3).
+    """
+    quaternions = random_quaternions(
+        n, dtype=dtype, device=device, requires_grad=requires_grad
+    )
+    return quaternion_to_matrix(quaternions)
+
+
+def random_rotation(
+    dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate a single random 3x3 rotation matrix.
+
+    Args:
+        dtype: Type to return
+        device: Device of returned tensor. Default: if None,
+            uses the current device for the default tensor type
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set
+
+    Returns:
+        Rotation matrix as tensor of shape (3, 3).
+    """
+    return random_rotations(1, dtype, device, requires_grad)[0]
+
+
+def standardize_quaternion(quaternions):
+    """
+    Convert a unit quaternion to a standard form: one in which the real
+    part is non negative.
+
+    Args:
+        quaternions: Quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Standardized quaternions as tensor of shape (..., 4).
+    """
+    return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
+
+
+def quaternion_raw_multiply(a, b):
+    """
+    Multiply two quaternions.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+
+    Returns:
+        The product of a and b, a tensor of quaternions shape (..., 4).
+    """
+    aw, ax, ay, az = torch.unbind(a, -1)
+    bw, bx, by, bz = torch.unbind(b, -1)
+    ow = aw * bw - ax * bx - ay * by - az * bz
+    ox = aw * bx + ax * bw + ay * bz - az * by
+    oy = aw * by - ax * bz + ay * bw + az * bx
+    oz = aw * bz + ax * by - ay * bx + az * bw
+    return torch.stack((ow, ox, oy, oz), -1)
+
+
+def quaternion_multiply(a, b):
+    """
+    Multiply two quaternions representing rotations, returning the quaternion
+    representing their composition, i.e. the versor with nonnegative real part.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+
+    Returns:
+        The product of a and b, a tensor of quaternions of shape (..., 4).
+    """
+    ab = quaternion_raw_multiply(a, b)
+    return standardize_quaternion(ab)
+
+
+def quaternion_invert(quaternion):
+    """
+    Given a quaternion representing rotation, get the quaternion representing
+    its inverse.
+
+    Args:
+        quaternion: Quaternions as tensor of shape (..., 4), with real part
+            first, which must be versors (unit quaternions).
+
+    Returns:
+        The inverse, a tensor of quaternions of shape (..., 4).
+    """
+
+    return quaternion * quaternion.new_tensor([1, -1, -1, -1])
+
+
+def quaternion_apply(quaternion, point):
+    """
+    Apply the rotation given by a quaternion to a 3D point.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        quaternion: Tensor of quaternions, real part first, of shape (..., 4).
+        point: Tensor of 3D points of shape (..., 3).
+
+    Returns:
+        Tensor of rotated points of shape (..., 3).
+    """
+    if point.size(-1) != 3:
+        raise ValueError(f"Points are not in 3D, f{point.shape}.")
+    real_parts = point.new_zeros(point.shape[:-1] + (1,))
+    point_as_quaternion = torch.cat((real_parts, point), -1)
+    out = quaternion_raw_multiply(
+        quaternion_raw_multiply(quaternion, point_as_quaternion),
+        quaternion_invert(quaternion),
+    )
+    return out[..., 1:]
+
+
+def axis_angle_to_matrix(axis_angle):
+    """
+    Convert rotations given as axis/angle to rotation matrices.
+
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
+
+
+def matrix_to_axis_angle(matrix):
+    """
+    Convert rotations given as rotation matrices to axis/angle.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
+
+
+def axis_angle_to_quaternion(axis_angle):
+    """
+    Convert rotations given as axis/angle to quaternions.
+
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
+    half_angles = 0.5 * angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+        0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    quaternions = torch.cat(
+        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
+    )
+    return quaternions
+
+
+def quaternion_to_axis_angle(quaternions):
+    """
+    Convert rotations given as quaternions to axis/angle.
+
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
+    half_angles = torch.atan2(norms, quaternions[..., :1])
+    angles = 2 * half_angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+        0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    return quaternions[..., 1:] / sin_half_angles_over_angles
+
+
+def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
+    """
+    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
+    using Gram--Schmidt orthogonalisation per Section B of [1].
+    Args:
+        d6: 6D rotation representation, of size (*, 6)
+
+    Returns:
+        batch of rotation matrices of size (*, 3, 3)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+
+    a1, a2 = d6[..., :3], d6[..., 3:]
+    b1 = F.normalize(a1, dim=-1)
+    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
+    b2 = F.normalize(b2, dim=-1)
+    b3 = torch.cross(b1, b2, dim=-1)
+    return torch.stack((b1, b2, b3), dim=-2)
+
+
+def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
+    by dropping the last row. Note that 6D representation is not unique.
+    Args:
+        matrix: batch of rotation matrices of size (*, 3, 3)
+
+    Returns:
+        6D rotation representation, of size (*, 6)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+    return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)

data_utils/split_more_than_2s.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2df6e745cdf7473f13ce3ae2ed759c3cceb60c9197e7f3fd65110e7bc20b6f2d
+size 2398875

data_utils/split_train_val_test.py

@@ -0,0 +1,27 @@
+import os 
+import json 
+import shutil
+
+if __name__ =='__main__':
+    id_list = "chemistry conan oliver seth"
+    id_list = id_list.split(' ')
+
+    old_root = '/home/usename/talkshow_data/ExpressiveWholeBodyDatasetReleaseV1.0'
+    new_root = '/home/usename/talkshow_data/ExpressiveWholeBodyDatasetReleaseV1.0/talkshow_data_splited'
+
+    with open('train_val_test.json') as f:
+        split_info = json.load(f)
+    phase_list = ['train', 'val', 'test']
+    for phase in phase_list:
+        phase_path_list = split_info[phase]
+        for p in phase_path_list:
+            old_path = os.path.join(old_root, p)
+            if not os.path.exists(old_path):
+                print(f'{old_path} not found, continue' )
+                continue
+            new_path = os.path.join(new_root, phase, p)
+            dir_name = os.path.dirname(new_path)
+            if not os.path.isdir(dir_name):
+                os.makedirs(dir_name, exist_ok=True)
+            shutil.move(old_path, new_path)
+

data_utils/utils.py

@@ -0,0 +1,318 @@
+import numpy as np
+# import librosa #has to do this cause librosa is not supported on my server
+import python_speech_features
+from scipy.io import wavfile
+from scipy import signal
+import librosa
+import torch
+import torchaudio as ta
+import torchaudio.functional as ta_F
+import torchaudio.transforms as ta_T
+# import pyloudnorm as pyln
+
+
+def load_wav_old(audio_fn, sr = 16000):
+    sample_rate, sig = wavfile.read(audio_fn)
+    if sample_rate != sr:
+        result = int((sig.shape[0]) / sample_rate * sr)
+        x_resampled = signal.resample(sig, result)
+        x_resampled = x_resampled.astype(np.float64)
+        return x_resampled, sr
+    
+    sig = sig / (2**15)
+    return sig, sample_rate
+
+
+def get_mfcc(audio_fn, eps=1e-6, fps=25, smlpx=False, sr=16000, n_mfcc=64, win_size=None):
+
+    y, sr = librosa.load(audio_fn, sr=sr, mono=True)
+
+    if win_size is None:
+        hop_len=int(sr / fps)
+    else:
+        hop_len=int(sr / win_size)
+        
+    n_fft=2048
+
+    C = librosa.feature.mfcc(
+        y = y,
+        sr = sr,
+        n_mfcc = n_mfcc,
+        hop_length = hop_len,
+        n_fft = n_fft
+    )
+
+    if C.shape[0] == n_mfcc:
+        C = C.transpose(1, 0)
+    
+    return C
+
+    
+def get_melspec(audio_fn, eps=1e-6, fps = 25, sr=16000, n_mels=64):
+    raise NotImplementedError
+    '''
+    # y, sr = load_wav(audio_fn=audio_fn, sr=sr)
+    
+    # hop_len = int(sr / fps) 
+    # n_fft = 2048
+
+    # C = librosa.feature.melspectrogram(
+    #     y = y, 
+    #     sr = sr, 
+    #     n_fft=n_fft, 
+    #     hop_length=hop_len, 
+    #     n_mels = n_mels, 
+    #     fmin=0, 
+    #     fmax=8000)
+    
+
+    # mask = (C == 0).astype(np.float)
+    # C = mask * eps + (1-mask) * C
+
+    # C = np.log(C)
+    # #wierd error may occur here
+    # assert not (np.isnan(C).any()), audio_fn
+    # if C.shape[0] == n_mels:
+    #     C = C.transpose(1, 0)
+
+    # return C 
+    '''
+
+def extract_mfcc(audio,sample_rate=16000):
+    mfcc = zip(*python_speech_features.mfcc(audio,sample_rate, numcep=64, nfilt=64, nfft=2048, winstep=0.04))
+    mfcc = np.stack([np.array(i) for i in mfcc])
+    return mfcc
+
+def get_mfcc_psf(audio_fn, eps=1e-6, fps=25, smlpx=False, sr=16000, n_mfcc=64, win_size=None):
+    y, sr = load_wav_old(audio_fn, sr=sr)
+
+    if y.shape.__len__() > 1:
+        y = (y[:,0]+y[:,1])/2
+
+    if win_size is None:
+        hop_len=int(sr / fps)
+    else:
+        hop_len=int(sr/ win_size)
+        
+    n_fft=2048 
+
+    #hard coded for 25 fps
+    if not smlpx:
+        C = python_speech_features.mfcc(y, sr, numcep=n_mfcc, nfilt=n_mfcc, nfft=n_fft, winstep=0.04)
+    else:
+        C = python_speech_features.mfcc(y, sr, numcep=n_mfcc, nfilt=n_mfcc, nfft=n_fft, winstep=1.01/15)
+    # if C.shape[0] == n_mfcc:
+    #     C = C.transpose(1, 0)
+    
+    return C
+
+
+def get_mfcc_psf_min(audio_fn, eps=1e-6, fps=25, smlpx=False, sr=16000, n_mfcc=64, win_size=None):
+    y, sr = load_wav_old(audio_fn, sr=sr)
+
+    if y.shape.__len__() > 1:
+        y = (y[:, 0] + y[:, 1]) / 2
+    n_fft = 2048
+
+    slice_len = 22000 * 5
+    slice = y.size // slice_len
+
+    C = []
+
+    for i in range(slice):
+        if i != (slice - 1):
+            feat = python_speech_features.mfcc(y[i*slice_len:(i+1)*slice_len], sr, numcep=n_mfcc, nfilt=n_mfcc, nfft=n_fft, winstep=1.01 / 15)
+        else:
+            feat = python_speech_features.mfcc(y[i * slice_len:], sr, numcep=n_mfcc, nfilt=n_mfcc, nfft=n_fft, winstep=1.01 / 15)
+
+        C.append(feat)
+
+    return C
+
+
+def audio_chunking(audio: torch.Tensor, frame_rate: int = 30, chunk_size: int = 16000):
+    """
+    :param audio: 1 x T tensor containing a 16kHz audio signal
+    :param frame_rate: frame rate for video (we need one audio chunk per video frame)
+    :param chunk_size: number of audio samples per chunk
+    :return: num_chunks x chunk_size tensor containing sliced audio
+    """
+    samples_per_frame = chunk_size // frame_rate
+    padding = (chunk_size - samples_per_frame) // 2
+    audio = torch.nn.functional.pad(audio.unsqueeze(0), pad=[padding, padding]).squeeze(0)
+    anchor_points = list(range(chunk_size//2, audio.shape[-1]-chunk_size//2, samples_per_frame))
+    audio = torch.cat([audio[:, i-chunk_size//2:i+chunk_size//2] for i in anchor_points], dim=0)
+    return audio
+
+
+def  get_mfcc_ta(audio_fn, eps=1e-6, fps=15, smlpx=False, sr=16000, n_mfcc=64, win_size=None, type='mfcc', am=None, am_sr=None, encoder_choice='mfcc'):
+    if am is None:
+        audio, sr_0 = ta.load(audio_fn)
+        if sr != sr_0:
+            audio = ta.transforms.Resample(sr_0, sr)(audio)
+        if audio.shape[0] > 1:
+            audio = torch.mean(audio, dim=0, keepdim=True)
+
+        n_fft = 2048
+        if fps == 15:
+            hop_length = 1467
+        elif fps == 30:
+            hop_length = 734
+        win_length = hop_length * 2
+        n_mels = 256
+        n_mfcc = 64
+
+        if type == 'mfcc':
+            mfcc_transform = ta_T.MFCC(
+                sample_rate=sr,
+                n_mfcc=n_mfcc,
+                melkwargs={
+                    "n_fft": n_fft,
+                    "n_mels": n_mels,
+                    # "win_length": win_length,
+                    "hop_length": hop_length,
+                    "mel_scale": "htk",
+                },
+            )
+            audio_ft = mfcc_transform(audio).squeeze(dim=0).transpose(0,1).numpy()
+        elif type == 'mel':
+            # audio = 0.01 * audio / torch.mean(torch.abs(audio))
+            mel_transform = ta_T.MelSpectrogram(
+                sample_rate=sr, n_fft=n_fft, win_length=None, hop_length=hop_length, n_mels=n_mels
+            )
+            audio_ft = mel_transform(audio).squeeze(0).transpose(0,1).numpy()
+            # audio_ft = torch.log(audio_ft.clamp(min=1e-10, max=None)).transpose(0,1).numpy()
+        elif type == 'mel_mul':
+            audio = 0.01 * audio / torch.mean(torch.abs(audio))
+            audio = audio_chunking(audio, frame_rate=fps, chunk_size=sr)
+            mel_transform = ta_T.MelSpectrogram(
+                sample_rate=sr, n_fft=n_fft, win_length=int(sr/20), hop_length=int(sr/100), n_mels=n_mels
+            )
+            audio_ft = mel_transform(audio).squeeze(1)
+            audio_ft = torch.log(audio_ft.clamp(min=1e-10, max=None)).numpy()
+    else:
+        speech_array, sampling_rate = librosa.load(audio_fn, sr=16000)
+
+        if encoder_choice == 'faceformer':
+            # audio_ft = np.squeeze(am(speech_array, sampling_rate=16000).input_values).reshape(-1, 1)
+            audio_ft = speech_array.reshape(-1, 1)
+        elif encoder_choice == 'meshtalk':
+            audio_ft = 0.01 * speech_array / np.mean(np.abs(speech_array))
+        elif encoder_choice == 'onset':
+            audio_ft = librosa.onset.onset_detect(y=speech_array, sr=16000, units='time').reshape(-1, 1)
+        else:
+            audio, sr_0 = ta.load(audio_fn)
+            if sr != sr_0:
+                audio = ta.transforms.Resample(sr_0, sr)(audio)
+            if audio.shape[0] > 1:
+                audio = torch.mean(audio, dim=0, keepdim=True)
+
+            n_fft = 2048
+            if fps == 15:
+                hop_length = 1467
+            elif fps == 30:
+                hop_length = 734
+            win_length = hop_length * 2
+            n_mels = 256
+            n_mfcc = 64
+
+            mfcc_transform = ta_T.MFCC(
+                sample_rate=sr,
+                n_mfcc=n_mfcc,
+                melkwargs={
+                    "n_fft": n_fft,
+                    "n_mels": n_mels,
+                    # "win_length": win_length,
+                    "hop_length": hop_length,
+                    "mel_scale": "htk",
+                },
+            )
+            audio_ft = mfcc_transform(audio).squeeze(dim=0).transpose(0, 1).numpy()
+    return audio_ft
+
+
+def  get_mfcc_sepa(audio_fn, fps=15, sr=16000):
+    audio, sr_0 = ta.load(audio_fn)
+    if sr != sr_0:
+        audio = ta.transforms.Resample(sr_0, sr)(audio)
+    if audio.shape[0] > 1:
+        audio = torch.mean(audio, dim=0, keepdim=True)
+
+    n_fft = 2048
+    if fps == 15:
+        hop_length = 1467
+    elif fps == 30:
+        hop_length = 734
+    n_mels = 256
+    n_mfcc = 64
+
+    mfcc_transform = ta_T.MFCC(
+        sample_rate=sr,
+        n_mfcc=n_mfcc,
+        melkwargs={
+            "n_fft": n_fft,
+            "n_mels": n_mels,
+            # "win_length": win_length,
+            "hop_length": hop_length,
+            "mel_scale": "htk",
+        },
+    )
+    audio_ft_0 = mfcc_transform(audio[0, :sr*2]).squeeze(dim=0).transpose(0,1).numpy()
+    audio_ft_1 = mfcc_transform(audio[0, sr*2:]).squeeze(dim=0).transpose(0,1).numpy()
+    audio_ft = np.concatenate((audio_ft_0, audio_ft_1), axis=0)
+    return audio_ft, audio_ft_0.shape[0]
+
+
+def get_mfcc_old(wav_file):
+    sig, sample_rate = load_wav_old(wav_file)
+    mfcc = extract_mfcc(sig)
+    return mfcc
+
+
+def smooth_geom(geom, mask: torch.Tensor = None, filter_size: int = 9, sigma: float = 2.0):
+    """
+    :param geom: T x V x 3 tensor containing a temporal sequence of length T with V vertices in each frame
+    :param mask: V-dimensional Tensor containing a mask with vertices to be smoothed
+    :param filter_size: size of the Gaussian filter
+    :param sigma: standard deviation of the Gaussian filter
+    :return: T x V x 3 tensor containing smoothed geometry (i.e., smoothed in the area indicated by the mask)
+    """
+    assert filter_size % 2 == 1, f"filter size must be odd but is {filter_size}"
+    # Gaussian smoothing (low-pass filtering)
+    fltr = np.arange(-(filter_size // 2), filter_size // 2 + 1)
+    fltr = np.exp(-0.5 * fltr ** 2 / sigma ** 2)
+    fltr = torch.Tensor(fltr) / np.sum(fltr)
+    # apply fltr
+    fltr = fltr.view(1, 1, -1).to(device=geom.device)
+    T, V = geom.shape[1], geom.shape[2]
+    g = torch.nn.functional.pad(
+        geom.permute(2, 0, 1).view(V, 1, T),
+        pad=[filter_size // 2, filter_size // 2], mode='replicate'
+    )
+    g = torch.nn.functional.conv1d(g, fltr).view(V, 1, T)
+    smoothed = g.permute(1, 2, 0).contiguous()
+    # blend smoothed signal with original signal
+    if mask is None:
+        return smoothed
+    else:
+        return smoothed * mask[None, :, None] + geom * (-mask[None, :, None] + 1)
+
+if __name__ == '__main__':
+    audio_fn = '../sample_audio/clip000028_tCAkv4ggPgI.wav'
+    
+    C = get_mfcc_psf(audio_fn)
+    print(C.shape)
+
+    C_2 = get_mfcc_librosa(audio_fn)
+    print(C.shape)
+
+    print(C)
+    print(C_2)
+    print((C == C_2).all())
+    # print(y.shape, sr)
+    # mel_spec = get_melspec(audio_fn)
+    # print(mel_spec.shape)
+    # mfcc = get_mfcc(audio_fn, sr = 16000)
+    # print(mfcc.shape)
+    # print(mel_spec.max(), mel_spec.min())
+    # print(mfcc.max(), mfcc.min())

evaluation/FGD.py

@@ -0,0 +1,199 @@
+import time
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from scipy import linalg
+import math
+from data_utils.rotation_conversion import axis_angle_to_matrix, matrix_to_rotation_6d
+
+import warnings
+warnings.filterwarnings("ignore", category=RuntimeWarning)  # ignore warnings
+
+
+change_angle = torch.tensor([6.0181e-05, 5.1597e-05, 2.1344e-04, 2.1899e-04])
+class EmbeddingSpaceEvaluator:
+    def __init__(self, ae, vae, device):
+
+        # init embed net
+        self.ae = ae
+        # self.vae = vae
+
+        # storage
+        self.real_feat_list = []
+        self.generated_feat_list = []
+        self.real_joints_list = []
+        self.generated_joints_list = []
+        self.real_6d_list = []
+        self.generated_6d_list = []
+        self.audio_beat_list = []
+
+    def reset(self):
+        self.real_feat_list = []
+        self.generated_feat_list = []
+
+    def get_no_of_samples(self):
+        return len(self.real_feat_list)
+
+    def push_samples(self, generated_poses, real_poses):
+        # self.net.eval()
+        # convert poses to latent features
+        real_feat, real_poses = self.ae.extract(real_poses)
+        generated_feat, generated_poses = self.ae.extract(generated_poses)
+
+        num_joints = real_poses.shape[2] // 3
+
+        real_feat = real_feat.squeeze()
+        generated_feat = generated_feat.reshape(generated_feat.shape[0]*generated_feat.shape[1], -1)
+
+        self.real_feat_list.append(real_feat.data.cpu().numpy())
+        self.generated_feat_list.append(generated_feat.data.cpu().numpy())
+
+        # real_poses = matrix_to_rotation_6d(axis_angle_to_matrix(real_poses.reshape(-1, 3))).reshape(-1, num_joints, 6)
+        # generated_poses = matrix_to_rotation_6d(axis_angle_to_matrix(generated_poses.reshape(-1, 3))).reshape(-1, num_joints, 6)
+        #
+        # self.real_feat_list.append(real_poses.data.cpu().numpy())
+        # self.generated_feat_list.append(generated_poses.data.cpu().numpy())
+
+    def push_joints(self, generated_poses, real_poses):
+        self.real_joints_list.append(real_poses.data.cpu())
+        self.generated_joints_list.append(generated_poses.squeeze().data.cpu())
+
+    def push_aud(self, aud):
+        self.audio_beat_list.append(aud.squeeze().data.cpu())
+
+    def get_MAAC(self):
+        ang_vel_list = []
+        for real_joints in self.real_joints_list:
+            real_joints[:, 15:21] = real_joints[:, 16:22]
+            vec = real_joints[:, 15:21] - real_joints[:, 13:19]
+            inner_product = torch.einsum('kij,kij->ki', [vec[:, 2:], vec[:, :-2]])
+            inner_product = torch.clamp(inner_product, -1, 1, out=None)
+            angle = torch.acos(inner_product) / math.pi
+            ang_vel = (angle[1:] - angle[:-1]).abs().mean(dim=0)
+            ang_vel_list.append(ang_vel.unsqueeze(dim=0))
+        all_vel = torch.cat(ang_vel_list, dim=0)
+        MAAC = all_vel.mean(dim=0)
+        return MAAC
+
+    def get_BCscore(self):
+        thres = 0.01
+        sigma = 0.1
+        sum_1 = 0
+        total_beat = 0
+        for joints, audio_beat_time in zip(self.generated_joints_list, self.audio_beat_list):
+            motion_beat_time = []
+            if joints.dim() == 4:
+                joints = joints[0]
+            joints[:, 15:21] = joints[:, 16:22]
+            vec = joints[:, 15:21] - joints[:, 13:19]
+            inner_product = torch.einsum('kij,kij->ki', [vec[:, 2:], vec[:, :-2]])
+            inner_product = torch.clamp(inner_product, -1, 1, out=None)
+            angle = torch.acos(inner_product) / math.pi
+            ang_vel = (angle[1:] - angle[:-1]).abs() / change_angle / len(change_angle)
+
+            angle_diff = torch.cat((torch.zeros(1, 4), ang_vel), dim=0)
+
+            sum_2 = 0
+            for i in range(angle_diff.shape[1]):
+                motion_beat_time = []
+                for t in range(1, joints.shape[0]-1):
+                    if (angle_diff[t][i] < angle_diff[t - 1][i] and angle_diff[t][i] < angle_diff[t + 1][i]):
+                        if (angle_diff[t - 1][i] - angle_diff[t][i] >= thres or angle_diff[t + 1][i] - angle_diff[
+                            t][i] >= thres):
+                            motion_beat_time.append(float(t) / 30.0)
+                if (len(motion_beat_time) == 0):
+                    continue
+                motion_beat_time = torch.tensor(motion_beat_time)
+                sum = 0
+                for audio in audio_beat_time:
+                    sum += np.power(math.e, -(np.power((audio.item() - motion_beat_time), 2)).min() / (2 * sigma * sigma))
+                sum_2 = sum_2 + sum
+                total_beat = total_beat + len(audio_beat_time)
+            sum_1 = sum_1 + sum_2
+        return sum_1/total_beat
+
+
+    def get_scores(self):
+        generated_feats = np.vstack(self.generated_feat_list)
+        real_feats = np.vstack(self.real_feat_list)
+
+        def frechet_distance(samples_A, samples_B):
+            A_mu = np.mean(samples_A, axis=0)
+            A_sigma = np.cov(samples_A, rowvar=False)
+            B_mu = np.mean(samples_B, axis=0)
+            B_sigma = np.cov(samples_B, rowvar=False)
+            try:
+                frechet_dist = self.calculate_frechet_distance(A_mu, A_sigma, B_mu, B_sigma)
+            except ValueError:
+                frechet_dist = 1e+10
+            return frechet_dist
+
+        ####################################################################
+        # frechet distance
+        frechet_dist = frechet_distance(generated_feats, real_feats)
+
+        ####################################################################
+        # distance between real and generated samples on the latent feature space
+        dists = []
+        for i in range(real_feats.shape[0]):
+            d = np.sum(np.absolute(real_feats[i] - generated_feats[i]))  # MAE
+            dists.append(d)
+        feat_dist = np.mean(dists)
+
+        return frechet_dist, feat_dist
+
+    @staticmethod
+    def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+        """ from https://github.com/mseitzer/pytorch-fid/blob/master/fid_score.py """
+        """Numpy implementation of the Frechet Distance.
+        The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+        and X_2 ~ N(mu_2, C_2) is
+                d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+        Stable version by Dougal J. Sutherland.
+        Params:
+        -- mu1   : Numpy array containing the activations of a layer of the
+                   inception net (like returned by the function 'get_predictions')
+                   for generated samples.
+        -- mu2   : The sample mean over activations, precalculated on an
+                   representative data set.
+        -- sigma1: The covariance matrix over activations for generated samples.
+        -- sigma2: The covariance matrix over activations, precalculated on an
+                   representative data set.
+        Returns:
+        --   : The Frechet Distance.
+        """
+
+        mu1 = np.atleast_1d(mu1)
+        mu2 = np.atleast_1d(mu2)
+
+        sigma1 = np.atleast_2d(sigma1)
+        sigma2 = np.atleast_2d(sigma2)
+
+        assert mu1.shape == mu2.shape, \
+            'Training and test mean vectors have different lengths'
+        assert sigma1.shape == sigma2.shape, \
+            'Training and test covariances have different dimensions'
+
+        diff = mu1 - mu2
+
+        # Product might be almost singular
+        covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+        if not np.isfinite(covmean).all():
+            msg = ('fid calculation produces singular product; '
+                   'adding %s to diagonal of cov estimates') % eps
+            print(msg)
+            offset = np.eye(sigma1.shape[0]) * eps
+            covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+        # Numerical error might give slight imaginary component
+        if np.iscomplexobj(covmean):
+            if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+                m = np.max(np.abs(covmean.imag))
+                raise ValueError('Imaginary component {}'.format(m))
+            covmean = covmean.real
+
+        tr_covmean = np.trace(covmean)
+
+        return (diff.dot(diff) + np.trace(sigma1) +
+                np.trace(sigma2) - 2 * tr_covmean)

evaluation/diversity_LVD.py

@@ -0,0 +1,64 @@
+'''
+LVD: different initial pose
+diversity: same initial pose
+'''
+import os
+import sys
+sys.path.append(os.getcwd())
+
+from glob import glob
+
+from argparse import ArgumentParser
+import json
+
+from evaluation.util import *
+from evaluation.metrics import *
+from tqdm import tqdm
+
+parser = ArgumentParser()
+parser.add_argument('--speaker', required=True, type=str)
+parser.add_argument('--post_fix', nargs='+', default=['base'], type=str)
+args = parser.parse_args()
+
+speaker = args.speaker
+test_audios = sorted(glob('pose_dataset/videos/test_audios/%s/*.wav'%(speaker)))
+
+LVD_list = []
+diversity_list = []
+
+for aud in tqdm(test_audios):
+    base_name = os.path.splitext(aud)[0]
+    gt_path = get_full_path(aud, speaker, 'val')
+    _, gt_poses, _ = get_gts(gt_path)
+    gt_poses = gt_poses[np.newaxis,...]
+    # print(gt_poses.shape)#(seq_len, 135*2)pose, lhand, rhand, face
+    for post_fix in args.post_fix:
+        pred_path = base_name + '_'+post_fix+'.json'
+        pred_poses = np.array(json.load(open(pred_path)))
+        # print(pred_poses.shape)#(B, seq_len, 108)
+        pred_poses = cvt25(pred_poses, gt_poses)
+        # print(pred_poses.shape)#(B, seq, pose_dim)
+
+        gt_valid_points = hand_points(gt_poses)
+        pred_valid_points = hand_points(pred_poses)
+
+        lvd = LVD(gt_valid_points, pred_valid_points)
+        # div = diversity(pred_valid_points)
+
+        LVD_list.append(lvd)
+        # diversity_list.append(div)
+
+        # gt_velocity = peak_velocity(gt_valid_points, order=2)
+        # pred_velocity = peak_velocity(pred_valid_points, order=2)
+
+        # gt_consistency = velocity_consistency(gt_velocity, pred_velocity)
+        # pred_consistency = velocity_consistency(pred_velocity, gt_velocity)
+
+        # gt_consistency_list.append(gt_consistency)
+        # pred_consistency_list.append(pred_consistency)
+
+lvd = np.mean(LVD_list)
+# diversity_list = np.mean(diversity_list)
+
+print('LVD:', lvd)
+# print("diversity:", diversity_list)

evaluation/get_quality_samples.py

@@ -0,0 +1,62 @@
+'''
+'''
+import os
+import sys
+sys.path.append(os.getcwd())
+
+from glob import glob
+
+from argparse import ArgumentParser
+import json
+
+from evaluation.util import *
+from evaluation.metrics import *
+from tqdm import tqdm
+
+parser = ArgumentParser()
+parser.add_argument('--speaker', required=True, type=str)
+parser.add_argument('--post_fix', nargs='+', default=['paper_model'], type=str)
+args = parser.parse_args()
+
+speaker = args.speaker
+test_audios = sorted(glob('pose_dataset/videos/test_audios/%s/*.wav'%(speaker)))
+
+quality_samples={'gt':[]}
+for post_fix in args.post_fix:
+    quality_samples[post_fix] = []
+
+for aud in tqdm(test_audios):
+    base_name = os.path.splitext(aud)[0]
+    gt_path = get_full_path(aud, speaker, 'val')
+    _, gt_poses, _ = get_gts(gt_path)
+    gt_poses = gt_poses[np.newaxis,...]
+    gt_valid_points = valid_points(gt_poses)
+    # print(gt_valid_points.shape)
+    quality_samples['gt'].append(gt_valid_points)
+
+    for post_fix in args.post_fix:
+        pred_path = base_name + '_'+post_fix+'.json'
+        pred_poses = np.array(json.load(open(pred_path)))
+        # print(pred_poses.shape)#(B, seq_len, 108)
+        pred_poses = cvt25(pred_poses, gt_poses)
+        # print(pred_poses.shape)#(B, seq, pose_dim)
+
+        pred_valid_points = valid_points(pred_poses)[0:1]
+        quality_samples[post_fix].append(pred_valid_points)
+
+quality_samples['gt'] = np.concatenate(quality_samples['gt'], axis=1)
+for post_fix in args.post_fix:
+    quality_samples[post_fix] = np.concatenate(quality_samples[post_fix], axis=1)
+
+print('gt:', quality_samples['gt'].shape)
+quality_samples['gt'] = quality_samples['gt'].tolist()
+for post_fix in args.post_fix:
+    print(post_fix, ':', quality_samples[post_fix].shape)
+    quality_samples[post_fix] = quality_samples[post_fix].tolist()
+
+save_dir = '../../experiments/'
+os.makedirs(save_dir, exist_ok=True)
+save_name = os.path.join(save_dir, 'quality_samples_%s.json'%(speaker))
+with open(save_name, 'w') as f:
+    json.dump(quality_samples, f)
+

evaluation/metrics.py

@@ -0,0 +1,109 @@
+'''
+Warning: metrics are for reference only, may have limited significance
+'''
+import os
+import sys
+sys.path.append(os.getcwd())
+import numpy as np
+import torch
+
+from data_utils.lower_body import rearrange, symmetry
+import torch.nn.functional as F
+
+def data_driven_baselines(gt_kps):
+    '''
+    gt_kps: T, D
+    '''
+    gt_velocity = np.abs(gt_kps[1:] - gt_kps[:-1])
+    
+    mean= np.mean(gt_velocity, axis=0)[np.newaxis] #(1, D)
+    mean = np.mean(np.abs(gt_velocity-mean))
+    last_step = gt_kps[1] - gt_kps[0]
+    last_step = last_step[np.newaxis] #(1, D)
+    last_step = np.mean(np.abs(gt_velocity-last_step))
+    return last_step, mean
+
+def Batch_LVD(gt_kps, pr_kps, symmetrical, weight):
+    if gt_kps.shape[0] > pr_kps.shape[1]:
+        length = pr_kps.shape[1]
+    else:
+        length = gt_kps.shape[0]
+    gt_kps = gt_kps[:length]
+    pr_kps = pr_kps[:, :length]
+    global symmetry
+    symmetry = torch.tensor(symmetry).bool()
+
+    if symmetrical:
+        # rearrange for compute symmetric. ns means non-symmetrical joints, ys means symmetrical joints.
+        gt_kps = gt_kps[:, rearrange]
+        ns_gt_kps = gt_kps[:, ~symmetry]
+        ys_gt_kps = gt_kps[:, symmetry]
+        ys_gt_kps = ys_gt_kps.reshape(ys_gt_kps.shape[0], -1, 2, 3)
+        ns_gt_velocity = (ns_gt_kps[1:] - ns_gt_kps[:-1]).norm(p=2, dim=-1)
+        ys_gt_velocity = (ys_gt_kps[1:] - ys_gt_kps[:-1]).norm(p=2, dim=-1)
+        left_gt_vel = ys_gt_velocity[:, :, 0].sum(dim=-1)
+        right_gt_vel = ys_gt_velocity[:, :, 1].sum(dim=-1)
+        move_side = torch.where(left_gt_vel>right_gt_vel, torch.ones(left_gt_vel.shape).cuda(),  torch.zeros(left_gt_vel.shape).cuda())
+        ys_gt_velocity = torch.mul(ys_gt_velocity[:, :, 0].transpose(0,1), move_side) + torch.mul(ys_gt_velocity[:, :, 1].transpose(0,1), ~move_side.bool())
+        ys_gt_velocity = ys_gt_velocity.transpose(0,1)
+        gt_velocity = torch.cat([ns_gt_velocity, ys_gt_velocity], dim=1)
+
+        pr_kps = pr_kps[:, :, rearrange]
+        ns_pr_kps = pr_kps[:, :, ~symmetry]
+        ys_pr_kps = pr_kps[:, :, symmetry]
+        ys_pr_kps = ys_pr_kps.reshape(ys_pr_kps.shape[0], ys_pr_kps.shape[1], -1, 2, 3)
+        ns_pr_velocity = (ns_pr_kps[:, 1:] - ns_pr_kps[:, :-1]).norm(p=2, dim=-1)
+        ys_pr_velocity = (ys_pr_kps[:, 1:] - ys_pr_kps[:, :-1]).norm(p=2, dim=-1)
+        left_pr_vel = ys_pr_velocity[:, :, :, 0].sum(dim=-1)
+        right_pr_vel = ys_pr_velocity[:, :, :, 1].sum(dim=-1)
+        move_side = torch.where(left_pr_vel > right_pr_vel, torch.ones(left_pr_vel.shape).cuda(),
+                                torch.zeros(left_pr_vel.shape).cuda())
+        ys_pr_velocity = torch.mul(ys_pr_velocity[..., 0].permute(2, 0, 1), move_side) + torch.mul(
+            ys_pr_velocity[..., 1].permute(2, 0, 1), ~move_side.long())
+        ys_pr_velocity = ys_pr_velocity.permute(1, 2, 0)
+        pr_velocity = torch.cat([ns_pr_velocity, ys_pr_velocity], dim=2)
+    else:
+        gt_velocity = (gt_kps[1:] - gt_kps[:-1]).norm(p=2, dim=-1)
+        pr_velocity = (pr_kps[:, 1:] - pr_kps[:, :-1]).norm(p=2, dim=-1)
+
+    if weight:
+        w = F.softmax(gt_velocity.sum(dim=1).normal_(), dim=0)
+    else:
+        w = 1 / gt_velocity.shape[0]
+
+    v_diff = ((pr_velocity - gt_velocity).abs().sum(dim=-1) * w).sum(dim=-1).mean()
+
+    return v_diff
+
+
+def LVD(gt_kps, pr_kps, symmetrical=False, weight=False):
+    gt_kps = gt_kps.squeeze()
+    pr_kps = pr_kps.squeeze()
+    if len(pr_kps.shape) == 4:
+        return Batch_LVD(gt_kps, pr_kps, symmetrical, weight)
+    # length = np.minimum(gt_kps.shape[0], pr_kps.shape[0])
+    length = gt_kps.shape[0]-10
+    # gt_kps = gt_kps[25:length]
+    # pr_kps = pr_kps[25:length] #(T, D)
+    # if pr_kps.shape[0] < gt_kps.shape[0]:
+    #     pr_kps = np.pad(pr_kps, [[0, int(gt_kps.shape[0]-pr_kps.shape[0])], [0, 0]], mode='constant')
+    
+    gt_velocity = (gt_kps[1:] - gt_kps[:-1]).norm(p=2, dim=-1)
+    pr_velocity = (pr_kps[1:] - pr_kps[:-1]).norm(p=2, dim=-1)
+    
+    return (pr_velocity-gt_velocity).abs().sum(dim=-1).mean()
+
+def diversity(kps):
+    '''
+    kps: bs, seq, dim
+    '''
+    dis_list = []
+    #the distance between each pair
+    for i in range(kps.shape[0]):
+        for j in range(i+1, kps.shape[0]):
+            seq_i = kps[i]
+            seq_j = kps[j]
+            
+            dis = np.mean(np.abs(seq_i - seq_j))
+            dis_list.append(dis)
+    return np.mean(dis_list)

evaluation/mode_transition.py

@@ -0,0 +1,60 @@
+import os
+import sys
+sys.path.append(os.getcwd())
+
+from glob import glob
+
+from argparse import ArgumentParser
+import json
+
+from evaluation.util import *
+from evaluation.metrics import *
+from tqdm import tqdm
+
+parser = ArgumentParser()
+parser.add_argument('--speaker', required=True, type=str)
+parser.add_argument('--post_fix', nargs='+', default=['paper_model'], type=str)
+args = parser.parse_args()
+
+speaker = args.speaker
+test_audios = sorted(glob('pose_dataset/videos/test_audios/%s/*.wav'%(speaker)))
+
+precision_list=[]
+recall_list=[]
+accuracy_list=[]
+
+for aud in tqdm(test_audios):
+    base_name = os.path.splitext(aud)[0]
+    gt_path = get_full_path(aud, speaker, 'val')
+    _, gt_poses, _ = get_gts(gt_path)
+    if gt_poses.shape[0] < 50:
+        continue
+    gt_poses = gt_poses[np.newaxis,...]
+    # print(gt_poses.shape)#(seq_len, 135*2)pose, lhand, rhand, face
+    for post_fix in args.post_fix:
+        pred_path = base_name + '_'+post_fix+'.json'
+        pred_poses = np.array(json.load(open(pred_path)))
+        # print(pred_poses.shape)#(B, seq_len, 108)
+        pred_poses = cvt25(pred_poses, gt_poses)
+        # print(pred_poses.shape)#(B, seq, pose_dim)
+
+        gt_valid_points = valid_points(gt_poses)
+        pred_valid_points = valid_points(pred_poses)
+
+        # print(gt_valid_points.shape, pred_valid_points.shape)
+
+        gt_mode_transition_seq = mode_transition_seq(gt_valid_points, speaker)#(B, N)
+        pred_mode_transition_seq = mode_transition_seq(pred_valid_points, speaker)#(B, N)
+
+        # baseline = np.random.randint(0, 2, size=pred_mode_transition_seq.shape)
+        # pred_mode_transition_seq = baseline
+        precision, recall, accuracy = mode_transition_consistency(pred_mode_transition_seq, gt_mode_transition_seq)
+        precision_list.append(precision)
+        recall_list.append(recall)
+        accuracy_list.append(accuracy)
+print(len(precision_list), len(recall_list), len(accuracy_list))
+precision_list = np.mean(precision_list)
+recall_list = np.mean(recall_list)
+accuracy_list = np.mean(accuracy_list)
+
+print('precision, recall, accu:', precision_list, recall_list, accuracy_list)

evaluation/peak_velocity.py

@@ -0,0 +1,65 @@
+import os
+import sys
+sys.path.append(os.getcwd())
+
+from glob import glob
+
+from argparse import ArgumentParser
+import json
+
+from evaluation.util import *
+from evaluation.metrics import *
+from tqdm import tqdm
+
+parser = ArgumentParser()
+parser.add_argument('--speaker', required=True, type=str)
+parser.add_argument('--post_fix', nargs='+', default=['paper_model'], type=str)
+args = parser.parse_args()
+
+speaker = args.speaker
+test_audios = sorted(glob('pose_dataset/videos/test_audios/%s/*.wav'%(speaker)))
+
+gt_consistency_list=[]
+pred_consistency_list=[]
+
+for aud in tqdm(test_audios):
+    base_name = os.path.splitext(aud)[0]
+    gt_path = get_full_path(aud, speaker, 'val')
+    _, gt_poses, _ = get_gts(gt_path)
+    gt_poses = gt_poses[np.newaxis,...]
+    # print(gt_poses.shape)#(seq_len, 135*2)pose, lhand, rhand, face
+    for post_fix in args.post_fix:
+        pred_path = base_name + '_'+post_fix+'.json'
+        pred_poses = np.array(json.load(open(pred_path)))
+        # print(pred_poses.shape)#(B, seq_len, 108)
+        pred_poses = cvt25(pred_poses, gt_poses)
+        # print(pred_poses.shape)#(B, seq, pose_dim)
+
+        gt_valid_points = hand_points(gt_poses)
+        pred_valid_points = hand_points(pred_poses)
+
+        gt_velocity = peak_velocity(gt_valid_points, order=2)
+        pred_velocity = peak_velocity(pred_valid_points, order=2)
+
+        gt_consistency = velocity_consistency(gt_velocity, pred_velocity)
+        pred_consistency = velocity_consistency(pred_velocity, gt_velocity)
+
+        gt_consistency_list.append(gt_consistency)
+        pred_consistency_list.append(pred_consistency)
+
+gt_consistency_list = np.concatenate(gt_consistency_list)
+pred_consistency_list = np.concatenate(pred_consistency_list)
+
+print(gt_consistency_list.max(), gt_consistency_list.min())
+print(pred_consistency_list.max(), pred_consistency_list.min())
+print(np.mean(gt_consistency_list), np.mean(pred_consistency_list))
+print(np.std(gt_consistency_list), np.std(pred_consistency_list))
+
+draw_cdf(gt_consistency_list, save_name='%s_gt.jpg'%(speaker), color='slateblue')
+draw_cdf(pred_consistency_list, save_name='%s_pred.jpg'%(speaker), color='lightskyblue')
+
+to_excel(gt_consistency_list, '%s_gt.xlsx'%(speaker))
+to_excel(pred_consistency_list, '%s_pred.xlsx'%(speaker))
+
+np.save('%s_gt.npy'%(speaker), gt_consistency_list)
+np.save('%s_pred.npy'%(speaker), pred_consistency_list)

evaluation/util.py

@@ -0,0 +1,148 @@
+import os
+from glob import glob
+import numpy as np
+import json
+from matplotlib import pyplot as plt
+import pandas as pd
+def get_gts(clip):    
+    '''
+    clip: abs path to the clip dir
+    '''
+    keypoints_files = sorted(glob(os.path.join(clip, 'keypoints_new/person_1')+'/*.json'))
+    
+    upper_body_points = list(np.arange(0, 25))
+    poses = [] 
+    confs = []
+    neck_to_nose_len = []
+    mean_position = []
+    for kp_file in keypoints_files:
+        kp_load = json.load(open(kp_file, 'r'))['people'][0]
+        posepts = kp_load['pose_keypoints_2d']
+        lhandpts = kp_load['hand_left_keypoints_2d']
+        rhandpts = kp_load['hand_right_keypoints_2d']
+        facepts = kp_load['face_keypoints_2d']
+
+        neck = np.array(posepts).reshape(-1,3)[1]
+        nose = np.array(posepts).reshape(-1,3)[0]
+        x_offset = abs(neck[0]-nose[0])
+        y_offset = abs(neck[1]-nose[1])
+        neck_to_nose_len.append(y_offset)
+        mean_position.append([neck[0],neck[1]])
+
+        keypoints=np.array(posepts+lhandpts+rhandpts+facepts).reshape(-1,3)[:,:2]
+
+        upper_body = keypoints[upper_body_points, :]
+        hand_points = keypoints[25:, :]
+        keypoints = np.vstack([upper_body, hand_points])
+
+        poses.append(keypoints)
+
+    if len(neck_to_nose_len) > 0:
+        scale_factor = np.mean(neck_to_nose_len)
+    else:
+        raise ValueError(clip)
+    mean_position = np.mean(np.array(mean_position), axis=0)
+    
+    unlocalized_poses = np.array(poses).copy()
+    localized_poses = []
+    for i in range(len(poses)):
+        keypoints = poses[i]
+        neck = keypoints[1].copy()
+
+        keypoints[:, 0] = (keypoints[:, 0] - neck[0]) / scale_factor
+        keypoints[:, 1] = (keypoints[:, 1] - neck[1]) / scale_factor
+        localized_poses.append(keypoints.reshape(-1))
+        
+    localized_poses=np.array(localized_poses)
+    return unlocalized_poses, localized_poses, (scale_factor, mean_position)
+
+def get_full_path(wav_name, speaker, split):
+    '''
+    get clip path from aud file
+    '''
+    wav_name = os.path.basename(wav_name)
+    wav_name = os.path.splitext(wav_name)[0]
+    clip_name, vid_name = wav_name[:10], wav_name[11:]
+
+    full_path = os.path.join('pose_dataset/videos/', speaker, 'clips', vid_name, 'images/half', split, clip_name)
+
+    assert os.path.isdir(full_path), full_path
+
+    return full_path
+
+def smooth(res):
+    '''
+    res: (B, seq_len, pose_dim)
+    '''
+    window = [res[:, 7, :], res[:, 8, :], res[:, 9, :], res[:, 10, :], res[:, 11, :], res[:, 12, :]]
+    w_size=7
+    for i in range(10, res.shape[1]-3):
+        window.append(res[:, i+3, :])
+        if len(window) > w_size:
+            window = window[1:]
+        
+        if (i%25) in [22, 23, 24, 0, 1, 2, 3]:
+            res[:, i, :] = np.mean(window, axis=1)
+    
+    return res
+
+def cvt25(pred_poses, gt_poses=None):
+    '''
+    gt_poses: (1, seq_len, 270), 135 *2
+    pred_poses: (B, seq_len, 108), 54 * 2
+    '''
+    if gt_poses is None:
+        gt_poses = np.zeros_like(pred_poses)
+    else:
+        gt_poses = gt_poses.repeat(pred_poses.shape[0], axis=0)
+
+    length = min(pred_poses.shape[1], gt_poses.shape[1])
+    pred_poses = pred_poses[:, :length, :]
+    gt_poses = gt_poses[:, :length, :]
+    gt_poses = gt_poses.reshape(gt_poses.shape[0], gt_poses.shape[1], -1, 2)
+    pred_poses = pred_poses.reshape(pred_poses.shape[0], pred_poses.shape[1], -1, 2)
+
+    gt_poses[:, :, [1, 2, 3, 4, 5, 6, 7], :] = pred_poses[:, :, 1:8, :]
+    gt_poses[:, :, 25:25+21+21, :] = pred_poses[:, :, 12:, :]
+    
+    return gt_poses.reshape(gt_poses.shape[0], gt_poses.shape[1], -1)
+
+def hand_points(seq):
+    '''
+    seq: (B, seq_len, 135*2)
+    hands only
+    '''
+    hand_idx = [1, 2, 3, 4,5 ,6,7] + list(range(25, 25+21+21))
+    seq = seq.reshape(seq.shape[0], seq.shape[1], -1, 2)
+    return seq[:, :, hand_idx, :].reshape(seq.shape[0], seq.shape[1], -1)
+
+def valid_points(seq):
+    '''
+    hands with some head points
+    '''
+    valid_idx = [0, 1, 2, 3, 4,5 ,6,7, 8, 9, 10, 11] + list(range(25, 25+21+21))
+    seq = seq.reshape(seq.shape[0], seq.shape[1], -1, 2)
+
+    seq = seq[:, :, valid_idx, :].reshape(seq.shape[0], seq.shape[1], -1)
+    assert seq.shape[-1] == 108, seq.shape
+    return seq
+
+def draw_cdf(seq, save_name='cdf.jpg', color='slatebule'):
+    plt.figure()
+    plt.hist(seq, bins=100, range=(0, 100), color=color)
+    plt.savefig(save_name)
+
+def to_excel(seq, save_name='res.xlsx'):
+    '''
+    seq: (T)
+    '''
+    df = pd.DataFrame(seq)
+    writer = pd.ExcelWriter(save_name)
+    df.to_excel(writer, 'sheet1')
+    writer.save()
+    writer.close()
+
+
+if __name__ == '__main__':
+    random_data = np.random.randint(0, 10, 100)
+    draw_cdf(random_data)

losses/__init__.py

@@ -0,0 +1 @@
+from .losses import *

losses/losses.py

@@ -0,0 +1,91 @@
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+class KeypointLoss(nn.Module):
+    def __init__(self):
+        super(KeypointLoss, self).__init__()
+    
+    def forward(self, pred_seq, gt_seq, gt_conf=None):
+        #pred_seq: (B, C, T)
+        if gt_conf is not None:
+            gt_conf = gt_conf >= 0.01
+            return F.mse_loss(pred_seq[gt_conf], gt_seq[gt_conf], reduction='mean')
+        else:
+            return F.mse_loss(pred_seq, gt_seq)
+
+
+class KLLoss(nn.Module):
+    def __init__(self, kl_tolerance):
+        super(KLLoss, self).__init__()
+        self.kl_tolerance = kl_tolerance
+
+    def forward(self, mu, var, mul=1):
+        kl_tolerance = self.kl_tolerance * mul * var.shape[1] / 64
+        kld_loss = -0.5 * torch.sum(1 + var - mu**2 - var.exp(), dim=1)
+        # kld_loss = -0.5 * torch.sum(1 + (var-1) - (mu) ** 2 - (var-1).exp(), dim=1)
+        if self.kl_tolerance is not None:
+            # above_line = kld_loss[kld_loss > self.kl_tolerance]
+            # if len(above_line) > 0:
+            #     kld_loss = torch.mean(kld_loss)
+            # else:
+            #     kld_loss = 0
+            kld_loss = torch.where(kld_loss > kl_tolerance, kld_loss, torch.tensor(kl_tolerance, device='cuda'))
+        # else:
+        kld_loss = torch.mean(kld_loss)
+        return kld_loss
+
+
+class L2KLLoss(nn.Module):
+    def __init__(self, kl_tolerance):
+        super(L2KLLoss, self).__init__()
+        self.kl_tolerance = kl_tolerance
+
+    def forward(self, x):
+        # TODO: check
+        kld_loss = torch.sum(x ** 2, dim=1)
+        if self.kl_tolerance is not None:
+            above_line = kld_loss[kld_loss > self.kl_tolerance]
+            if len(above_line) > 0:
+                kld_loss = torch.mean(kld_loss)
+            else:
+                kld_loss = 0
+        else:
+            kld_loss = torch.mean(kld_loss)
+        return kld_loss
+
+class L2RegLoss(nn.Module):
+    def __init__(self):
+        super(L2RegLoss, self).__init__()
+    
+    def forward(self, x):
+        #TODO: check
+        return torch.sum(x**2)
+
+
+class L2Loss(nn.Module):
+    def __init__(self):
+        super(L2Loss, self).__init__()
+
+    def forward(self, x):
+        # TODO: check
+        return torch.sum(x ** 2)
+
+
+class AudioLoss(nn.Module):
+    def __init__(self):
+        super(AudioLoss, self).__init__()
+    
+    def forward(self, dynamics, gt_poses):
+        #pay attention, normalized
+        mean = torch.mean(gt_poses, dim=-1).unsqueeze(-1)
+        gt = gt_poses - mean
+        return F.mse_loss(dynamics, gt)
+
+L1Loss = nn.L1Loss

nets/LS3DCG.py

@@ -0,0 +1,414 @@
+'''
+not exactly the same as the official repo but the results are good
+'''
+import sys
+import os
+
+from data_utils.lower_body import c_index_3d, c_index_6d
+
+sys.path.append(os.getcwd())
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import math
+
+from nets.base import TrainWrapperBaseClass
+from nets.layers import SeqEncoder1D
+from losses import KeypointLoss, L1Loss, KLLoss
+from data_utils.utils import get_melspec, get_mfcc_psf, get_mfcc_ta
+from nets.utils import denormalize
+
+class Conv1d_tf(nn.Conv1d):
+    """
+    Conv1d with the padding behavior from TF
+    modified from https://github.com/mlperf/inference/blob/482f6a3beb7af2fb0bd2d91d6185d5e71c22c55f/others/edge/object_detection/ssd_mobilenet/pytorch/utils.py
+    """
+
+    def __init__(self, *args, **kwargs):
+        super(Conv1d_tf, self).__init__(*args, **kwargs)
+        self.padding = kwargs.get("padding", "same")
+
+    def _compute_padding(self, input, dim):
+        input_size = input.size(dim + 2)
+        filter_size = self.weight.size(dim + 2)
+        effective_filter_size = (filter_size - 1) * self.dilation[dim] + 1
+        out_size = (input_size + self.stride[dim] - 1) // self.stride[dim]
+        total_padding = max(
+            0, (out_size - 1) * self.stride[dim] + effective_filter_size - input_size
+        )
+        additional_padding = int(total_padding % 2 != 0)
+
+        return additional_padding, total_padding
+
+    def forward(self, input):
+        if self.padding == "VALID":
+            return F.conv1d(
+                input,
+                self.weight,
+                self.bias,
+                self.stride,
+                padding=0,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+        rows_odd, padding_rows = self._compute_padding(input, dim=0)
+        if rows_odd:
+            input = F.pad(input, [0, rows_odd])
+
+        return F.conv1d(
+            input,
+            self.weight,
+            self.bias,
+            self.stride,
+            padding=(padding_rows // 2),
+            dilation=self.dilation,
+            groups=self.groups,
+        )
+
+
+def ConvNormRelu(in_channels, out_channels, type='1d', downsample=False, k=None, s=None, norm='bn', padding='valid'):
+    if k is None and s is None:
+        if not downsample:
+            k = 3
+            s = 1
+        else:
+            k = 4
+            s = 2
+
+    if type == '1d':
+        conv_block = Conv1d_tf(in_channels, out_channels, kernel_size=k, stride=s, padding=padding)
+        if norm == 'bn':
+            norm_block = nn.BatchNorm1d(out_channels)
+        elif norm == 'ln':
+            norm_block = nn.LayerNorm(out_channels)
+    elif type == '2d':
+        conv_block = Conv2d_tf(in_channels, out_channels, kernel_size=k, stride=s, padding=padding)
+        norm_block = nn.BatchNorm2d(out_channels)
+    else:
+        assert False
+
+    return nn.Sequential(
+        conv_block,
+        norm_block,
+        nn.LeakyReLU(0.2, True)
+    )
+
+class Decoder(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super(Decoder, self).__init__()
+        self.up1 = nn.Sequential(
+            ConvNormRelu(in_ch // 2 + in_ch, in_ch // 2),
+            ConvNormRelu(in_ch // 2, in_ch // 2),
+            nn.Upsample(scale_factor=2, mode='nearest')
+        )
+        self.up2 = nn.Sequential(
+            ConvNormRelu(in_ch // 4 + in_ch // 2, in_ch // 4),
+            ConvNormRelu(in_ch // 4, in_ch // 4),
+            nn.Upsample(scale_factor=2, mode='nearest')
+        )
+        self.up3 = nn.Sequential(
+            ConvNormRelu(in_ch // 8 + in_ch // 4, in_ch // 8),
+            ConvNormRelu(in_ch // 8, in_ch // 8),
+            nn.Conv1d(in_ch // 8, out_ch, 1, 1)
+        )
+
+    def forward(self, x, x1, x2, x3):
+        x = F.interpolate(x, x3.shape[2])
+        x = torch.cat([x, x3], dim=1)
+        x = self.up1(x)
+        x = F.interpolate(x, x2.shape[2])
+        x = torch.cat([x, x2], dim=1)
+        x = self.up2(x)
+        x = F.interpolate(x, x1.shape[2])
+        x = torch.cat([x, x1], dim=1)
+        x = self.up3(x)
+        return x
+
+
+class EncoderDecoder(nn.Module):
+    def __init__(self, n_frames, each_dim):
+        super().__init__()
+        self.n_frames = n_frames
+
+        self.down1 = nn.Sequential(
+            ConvNormRelu(64, 64, '1d', False),
+            ConvNormRelu(64, 128, '1d', False),
+        )
+        self.down2 = nn.Sequential(
+            ConvNormRelu(128, 128, '1d', False),
+            ConvNormRelu(128, 256, '1d', False),
+        )
+        self.down3 = nn.Sequential(
+            ConvNormRelu(256, 256, '1d', False),
+            ConvNormRelu(256, 512, '1d', False),
+        )
+        self.down4 = nn.Sequential(
+            ConvNormRelu(512, 512, '1d', False),
+            ConvNormRelu(512, 1024, '1d', False),
+        )
+
+        self.down = nn.MaxPool1d(kernel_size=2)
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+
+        self.face_decoder = Decoder(1024, each_dim[0] + each_dim[3])
+        self.body_decoder = Decoder(1024, each_dim[1])
+        self.hand_decoder = Decoder(1024, each_dim[2])
+
+    def forward(self, spectrogram, time_steps=None):
+        if time_steps is None:
+            time_steps = self.n_frames
+
+        x1 = self.down1(spectrogram)
+        x = self.down(x1)
+        x2 = self.down2(x)
+        x = self.down(x2)
+        x3 = self.down3(x)
+        x = self.down(x3)
+        x = self.down4(x)
+        x = self.up(x)
+
+        face = self.face_decoder(x, x1, x2, x3)
+        body = self.body_decoder(x, x1, x2, x3)
+        hand = self.hand_decoder(x, x1, x2, x3)
+
+        return face, body, hand
+
+
+class Generator(nn.Module):
+    def __init__(self,
+                 each_dim,
+                 training=False,
+                 device=None
+                 ):
+        super().__init__()
+
+        self.training = training
+        self.device = device
+
+        self.encoderdecoder = EncoderDecoder(15, each_dim)
+
+    def forward(self, in_spec, time_steps=None):
+        if time_steps is not None:
+            self.gen_length = time_steps
+
+        face, body, hand = self.encoderdecoder(in_spec)
+        out = torch.cat([face, body, hand], dim=1)
+        out = out.transpose(1, 2)
+
+        return out
+
+
+class Discriminator(nn.Module):
+    def __init__(self, input_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            ConvNormRelu(input_dim, 128, '1d'),
+            ConvNormRelu(128, 256, '1d'),
+            nn.MaxPool1d(kernel_size=2),
+            ConvNormRelu(256, 256, '1d'),
+            ConvNormRelu(256, 512, '1d'),
+            nn.MaxPool1d(kernel_size=2),
+            ConvNormRelu(512, 512, '1d'),
+            ConvNormRelu(512, 1024, '1d'),
+            nn.MaxPool1d(kernel_size=2),
+            nn.Conv1d(1024, 1, 1, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+
+        out = self.net(x)
+        return out
+
+
+class TrainWrapper(TrainWrapperBaseClass):
+    def __init__(self, args, config) -> None:
+        self.args = args
+        self.config = config
+        self.device = torch.device(self.args.gpu)
+        self.global_step = 0
+        self.convert_to_6d = self.config.Data.pose.convert_to_6d
+        self.init_params()
+
+        self.generator = Generator(
+            each_dim=self.each_dim,
+            training=not self.args.infer,
+            device=self.device,
+        ).to(self.device)
+        self.discriminator = Discriminator(
+            input_dim=self.each_dim[1] + self.each_dim[2] + 64
+        ).to(self.device)
+        if self.convert_to_6d:
+            self.c_index = c_index_6d
+        else:
+            self.c_index = c_index_3d
+        self.MSELoss = KeypointLoss().to(self.device)
+        self.L1Loss = L1Loss().to(self.device)
+        super().__init__(args, config)
+
+    def init_params(self):
+        scale = 1
+
+        global_orient = round(0 * scale)
+        leye_pose = reye_pose = round(0 * scale)
+        jaw_pose = round(3 * scale)
+        body_pose = round((63 - 24) * scale)
+        left_hand_pose = right_hand_pose = round(45 * scale)
+
+        expression = 100
+
+        b_j = 0
+        jaw_dim = jaw_pose
+        b_e = b_j + jaw_dim
+        eye_dim = leye_pose + reye_pose
+        b_b = b_e + eye_dim
+        body_dim = global_orient + body_pose
+        b_h = b_b + body_dim
+        hand_dim = left_hand_pose + right_hand_pose
+        b_f = b_h + hand_dim
+        face_dim = expression
+
+        self.dim_list = [b_j, b_e, b_b, b_h, b_f]
+        self.full_dim = jaw_dim + eye_dim + body_dim + hand_dim
+        self.pose = int(self.full_dim / round(3 * scale))
+        self.each_dim = [jaw_dim, eye_dim + body_dim, hand_dim, face_dim]
+
+    def __call__(self, bat):
+        assert (not self.args.infer), "infer mode"
+        self.global_step += 1
+
+        loss_dict = {}
+
+        aud, poses = bat['aud_feat'].to(self.device).to(torch.float32), bat['poses'].to(self.device).to(torch.float32)
+        expression = bat['expression'].to(self.device).to(torch.float32)
+        jaw = poses[:, :3, :]
+        poses = poses[:, self.c_index, :]
+
+        pred = self.generator(in_spec=aud)
+
+        D_loss, D_loss_dict = self.get_loss(
+            pred_poses=pred.detach(),
+            gt_poses=poses,
+            aud=aud,
+            mode='training_D',
+        )
+
+        self.discriminator_optimizer.zero_grad()
+        D_loss.backward()
+        self.discriminator_optimizer.step()
+
+        G_loss, G_loss_dict = self.get_loss(
+            pred_poses=pred,
+            gt_poses=poses,
+            aud=aud,
+            expression=expression,
+            jaw=jaw,
+            mode='training_G',
+        )
+        self.generator_optimizer.zero_grad()
+        G_loss.backward()
+        self.generator_optimizer.step()
+
+        total_loss = None
+        loss_dict = {}
+        for key in list(D_loss_dict.keys()) + list(G_loss_dict.keys()):
+            loss_dict[key] = G_loss_dict.get(key, 0) + D_loss_dict.get(key, 0)
+
+        return total_loss, loss_dict
+
+    def get_loss(self,
+                 pred_poses,
+                 gt_poses,
+                 aud=None,
+                 jaw=None,
+                 expression=None,
+                 mode='training_G',
+                 ):
+        loss_dict = {}
+        aud = aud.transpose(1, 2)
+        gt_poses = gt_poses.transpose(1, 2)
+        gt_aud = torch.cat([gt_poses, aud], dim=2)
+        pred_aud = torch.cat([pred_poses[:, :, 103:], aud], dim=2)
+
+        if mode == 'training_D':
+            dis_real = self.discriminator(gt_aud)
+            dis_fake = self.discriminator(pred_aud)
+            dis_error = self.MSELoss(torch.ones_like(dis_real).to(self.device), dis_real) + self.MSELoss(
+                torch.zeros_like(dis_fake).to(self.device), dis_fake)
+            loss_dict['dis'] = dis_error
+
+            return dis_error, loss_dict
+        elif mode == 'training_G':
+            jaw_loss = self.L1Loss(pred_poses[:, :, :3], jaw.transpose(1, 2))
+            face_loss = self.MSELoss(pred_poses[:, :, 3:103], expression.transpose(1, 2))
+            body_loss = self.L1Loss(pred_poses[:, :, 103:142], gt_poses[:, :, :39])
+            hand_loss = self.L1Loss(pred_poses[:, :, 142:], gt_poses[:, :, 39:])
+            l1_loss = jaw_loss + face_loss + body_loss + hand_loss
+
+            dis_output = self.discriminator(pred_aud)
+            gen_error = self.MSELoss(torch.ones_like(dis_output).to(self.device), dis_output)
+            gen_loss = self.config.Train.weights.keypoint_loss_weight * l1_loss + self.config.Train.weights.gan_loss_weight * gen_error
+
+            loss_dict['gen'] = gen_error
+            loss_dict['jaw_loss'] = jaw_loss
+            loss_dict['face_loss'] = face_loss
+            loss_dict['body_loss'] = body_loss
+            loss_dict['hand_loss'] = hand_loss
+            return gen_loss, loss_dict
+        else:
+            raise ValueError(mode)
+
+    def infer_on_audio(self, aud_fn, fps=30, initial_pose=None, norm_stats=None, id=None, B=1, **kwargs):
+        output = []
+        assert self.args.infer, "train mode"
+        self.generator.eval()
+
+        if self.config.Data.pose.normalization:
+            assert norm_stats is not None
+            data_mean = norm_stats[0]
+            data_std = norm_stats[1]
+
+        pre_length = self.config.Data.pose.pre_pose_length
+        generate_length = self.config.Data.pose.generate_length
+        # assert pre_length == initial_pose.shape[-1]
+        # pre_poses = initial_pose.permute(0, 2, 1).to(self.device).to(torch.float32)
+        # B = pre_poses.shape[0]
+
+        aud_feat = get_mfcc_ta(aud_fn, sr=22000, fps=fps, smlpx=True, type='mfcc').transpose(1, 0)
+        num_poses_to_generate = aud_feat.shape[-1]
+        aud_feat = aud_feat[np.newaxis, ...].repeat(B, axis=0)
+        aud_feat = torch.tensor(aud_feat, dtype=torch.float32).to(self.device)
+
+        with torch.no_grad():
+            pred_poses = self.generator(aud_feat)
+            pred_poses = pred_poses.cpu().numpy()
+        output = pred_poses.squeeze()
+
+        return output
+
+    def generate(self, aud, id):
+        self.generator.eval()
+        pred_poses = self.generator(aud)
+        return pred_poses
+
+
+if __name__ == '__main__':
+    from trainer.options import parse_args
+
+    parser = parse_args()
+    args = parser.parse_args(
+        ['--exp_name', '0', '--data_root', '0', '--speakers', '0', '--pre_pose_length', '4', '--generate_length', '64',
+         '--infer'])
+
+    generator = TrainWrapper(args)
+
+    aud_fn = '../sample_audio/jon.wav'
+    initial_pose = torch.randn(64, 108, 4)
+    norm_stats = (np.random.randn(108), np.random.randn(108))
+    output = generator.infer_on_audio(aud_fn, initial_pose, norm_stats)
+
+    print(output.shape)

nets/__init__.py

@@ -0,0 +1,8 @@
+from .smplx_face import TrainWrapper as s2g_face
+from .smplx_body_vq import TrainWrapper as s2g_body_vq
+from .smplx_body_pixel import TrainWrapper as s2g_body_pixel
+from .body_ae import TrainWrapper as s2g_body_ae
+from .LS3DCG import TrainWrapper as LS3DCG
+from .base import TrainWrapperBaseClass
+
+from .utils import normalize, denormalize