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-# Convolutional Reconstruction Model
-
-Official implementation for *CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model*.
-
-**CRM is a feed-forward model which can generate 3D textured mesh in 10 seconds.**
-
-## [Project Page](https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/) | [Arxiv](https://arxiv.org/abs/2403.05034) | [HF-Demo](https://huggingface.co/spaces/Zhengyi/CRM) | [Weights](https://huggingface.co/Zhengyi/CRM)
-
-https://github.com/thu-ml/CRM/assets/40787266/8b325bc0-aa74-4c26-92e8-a8f0c1079382
-
-## Try CRM 🍻
-* Try CRM at [Huggingface Demo](https://huggingface.co/spaces/Zhengyi/CRM).
-* Try CRM at [Replicate Demo](https://replicate.com/camenduru/crm). Thanks [@camenduru](https://github.com/camenduru)! 
-
-## Install
-
-### Step 1 - Base
-
-Install package one by one, we use **python 3.9**
-
-```bash
-pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
-pip install torch-scatter==2.1.1 -f https://data.pyg.org/whl/torch-1.13.1+cu117.html
-pip install kaolin==0.14.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.13.1_cu117.html
-pip install -r requirements.txt
-```
-
-besides, one by one need to install xformers manually according to the official [doc](https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers) (**conda no need**), e.g.
-
-```bash
-pip install ninja
-pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
-```
-
-### Step 2 - Nvdiffrast
-
-Install nvdiffrast according to the official [doc](https://nvlabs.github.io/nvdiffrast/#installation), e.g.
-
-```bash
-pip install git+https://github.com/NVlabs/nvdiffrast
-```
-
-
-
-## Inference
-
-We suggest gradio for a visualized inference.
-
-```
-gradio app.py
-```
-
-![image](https://github.com/thu-ml/CRM/assets/40787266/4354d22a-a641-4531-8408-c761ead8b1a2)
-
-For inference in command lines, simply run
-```bash
-CUDA_VISIBLE_DEVICES="0" python run.py --inputdir "examples/kunkun.webp"
-```
-It will output the preprocessed image, generated 6-view images and CCMs and a 3D model in obj format.
-
-**Tips:** (1) If the result is unsatisfatory, please check whether the input image is correctly pre-processed into a grey background. Otherwise the results will be unpredictable.
-(2) Different from the [Huggingface Demo](https://huggingface.co/spaces/Zhengyi/CRM), this official implementation uses UV texture instead of vertex color. It has better texture than the online demo but longer generating time owing to the UV texturing.
-
-## Train
-We provide training script for multivew generation and their data requirements.
-To launch a simple one instance overfit training of multivew gen:
-```shell
-accelerate launch $accelerate_args train.py --config configs/nf7_v3_SNR_rd_size_stroke_train.yaml \
-    config.batch_size=1 \
-    config.eval_interval=100
-```
-To launch a simple one instance overfit training of CCM gen:
-```shell
-accelerate launch $accelerate_args train_stage2.py --config configs/stage2-v2-snr_train.yaml \
-    config.batch_size=1 \
-    config.eval_interval=100
-```
-
-### data prepare
-To specify the data dir modify the following params in the configs/xxxx.yaml
-```yaml
-    base_dir: <path to multiview piexl image basedir>
-    xyz_base: <path to related CCM image basedir>
-    caption_csv: <path to caption.csv>
-```
-The file tree of basedirs should satisfy as following:
-```shell
-base_dir
-├── uid1
-│   ├── 000.png
-│   ├── 001.png
-│   ├── 002.png
-│   ├── 003.png
-│   ├── 004.png
-│   ├── 005.png
-├── uid2
-....
-
-xyz_base
-├── uid1
-│   ├── xyz_new_000.png
-│   ├── xyz_new_001.png
-│   ├── xyz_new_002.png
-│   ├── xyz_new_003.png
-│   ├── xyz_new_004.png
-│   └── xyz_new_005.png
-├── uid2
-....
-```
-The `train_example` dir shows a minimal case of train data and `caption.csv` file.
-
-
-
-## Todo List
-- [x] Release inference code.
-- [x] Release pretrained models.
-- [ ] Optimize inference code to fit in low memery GPU.
-- [x] Upload training code.
-
-## Acknowledgement
-- [ImageDream](https://github.com/bytedance/ImageDream)
-- [nvdiffrast](https://github.com/NVlabs/nvdiffrast)
-- [kiuikit](https://github.com/ashawkey/kiuikit)
-- [GET3D](https://github.com/nv-tlabs/GET3D)
-
-## Citation
-
-```
-@article{wang2024crm,
-  title={CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model},
-  author={Zhengyi Wang and Yikai Wang and Yifei Chen and Chendong Xiang and Shuo Chen and Dajiang Yu and Chongxuan Li and Hang Su and Jun Zhu},
-  journal={arXiv preprint arXiv:2403.05034},
-  year={2024}
-}
-```