| # 3D Human Pose Estimation |
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| ## Data |
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| 1. Download the finetuned Stacked Hourglass detections and our preprocessed H3.6M data (.pkl) [here](https://1drv.ms/u/s!AvAdh0LSjEOlgSMvoapR8XVTGcVj) and put it to `data/motion3d`. |
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| > Note that the preprocessed data is only intended for reproducing our results more easily. If you want to use the dataset, please register to the [Human3.6m website](http://vision.imar.ro/human3.6m/) and download the dataset in its original format. Please refer to [LCN](https://github.com/CHUNYUWANG/lcn-pose#data) for how we prepare the H3.6M data. |
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| 2. Slice the motion clips (len=243, stride=81) |
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| ```bash |
| python tools/convert_h36m.py |
| ``` |
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| ## Running |
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| **Train from scratch:** |
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| ```bash |
| python train.py \ |
| --config configs/pose3d/MB_train_h36m.yaml \ |
| --checkpoint checkpoint/pose3d/MB_train_h36m |
| ``` |
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| **Finetune from pretrained MotionBERT:** |
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| ```bash |
| python train.py \ |
| --config configs/pose3d/MB_ft_h36m.yaml \ |
| --pretrained checkpoint/pretrain/MB_release \ |
| --checkpoint checkpoint/pose3d/FT_MB_release_MB_ft_h36m |
| ``` |
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| **Evaluate:** |
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| ```bash |
| python train.py \ |
| --config configs/pose3d/MB_train_h36m.yaml \ |
| --evaluate checkpoint/pose3d/MB_train_h36m/best_epoch.bin |
| ``` |
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