Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation

This is the official implementation of the approach described in the paper:

Wenhao Li, Hong Liu, Runwei Ding, Mengyuan Liu, Pichao Wang, and Wenming Yang. Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation. IEEE Transactions on Multimedia, 2022.

Recently, our method has been verified in self-supervised pre-training as a backbone network!

Dependencies

Cuda 11.1
Python 3.6
Pytorch 1.7.1

Dataset setup

Please download the dataset from Human3.6M website and refer to VideoPose3D to set up the Human3.6M dataset ('./dataset' directory).

${POSE_ROOT}/
|-- dataset
|   |-- data_3d_h36m.npz
|   |-- data_2d_h36m_cpn_ft_h36m_dbb.npz

Download pretrained model

The pretrained model can be found in Google_Drive, please download it and put in the './checkpoint' dictory.

Test the model

To test on pretrained model on Human3.6M:

python main.py --refine --reload --refine_reload --previous_dir 'checkpoint/pretrained'

Train the model

To train on Human3.6M:

python main.py --train

After training for several epoches, add refine module

python main.py --train --refine --lr 1e-5 --reload --previous_dir [your model saved path]

Citation

If you find our work useful in your research, please consider citing:

@article{li2022exploiting,
  title={Exploiting temporal contexts with strided transformer for 3d human pose estimation},
  author={Li, Wenhao and Liu, Hong and Ding, Runwei and Liu, Mengyuan and Wang, Pichao and Yang, Wenming},
  journal={IEEE Transactions on Multimedia},
  year={2022},
}

Acknowledgement

Our code is built on top of ST-GCN and is extended from the following repositories. We thank the authors for releasing the codes.

Licence

This project is licensed under the terms of the MIT license.

hopeliu20160622/StridedTransformer-Pose3D