3D human pose estimation in video with temporal convolutions

This implementation mainly trains the VideoPose3D in a supervised manner, modified from https://github.com/facebookresearch/VideoPose3D

VideoPose3D Details

Due to the Ascend-Python's low-efficient implementation on Conv1D, this version changes all conv1d to conv2d.

Results on Human3.6M

Under Protocol 1 (mean per-joint position error).

results	FPS	epoches	AMP_Type	Device(s)
46.5mm	5544	80	O1	1p NPU
46.79 mm	40757	80	O1	8p NPU

Quick start

To get started as quickly as possible, follow the instructions in this section. This should allow you train a model from scratch, test our pretrained models, and produce basic visualizations. For more detailed instructions, please refer to DOCUMENTATION.md.

Dependencies

Make sure you have the following dependencies installed before proceeding:

Python 3+ distribution
PyTorch 1.5.0
pip install -r requirements.txt

Dataset setup

In order to proceed, you must also copy the 'data' file we provided into the VideoPose3D root directory.

Train

# 1p train perf
bash test/train_performance_1p.sh

# 8p train perf
bash test/train_performance_8p.sh

# 1p train full
bash test/train_full_1p.sh

# 8p train full
bash test/train_full_8p.sh

# 8p evaluate full
bash test/eval_full_8p.sh

License

This work is licensed under CC BY-NC. See LICENSE for details. Third-party datasets are subject to their respective licenses. If you use our code/models in your research, please cite our paper:

@inproceedings{pavllo:videopose3d:2019,
  title={3D human pose estimation in video with temporal convolutions and semi-supervised training},
  author={Pavllo, Dario and Feichtenhofer, Christoph and Grangier, David and Auli, Michael},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}

Statement

For details about the public address of the code in this repository, you can get from the file public_address_statement.md