Awesome

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.

News

Our method has been verified in self-supervised pre-training as a backbone network!

Installation

Our code is tested on Ubuntu 18 with Pytorch 1.7.1 and Python 3.9.

Install PyTorch 1.7.1 and Torchvision 0.8.2 following the official instructions
pip3 install -r requirements.txt

Dataset setup

Please download the dataset from Human3.6M website and refer to VideoPose3D to set up the Human3.6M dataset ('./dataset' directory). Or you can download the processed data from here.

${POSE_ROOT}/
|-- dataset
|   |-- data_3d_h36m.npz
|   |-- data_2d_h36m_gt.npz
|   |-- data_2d_h36m_cpn_ft_h36m_dbb.npz

Download pretrained model

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

Test the model

To test on pretrained model on Human3.6M:

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

Train the model

To train on Human3.6M:

python main.py

After training for several epochs, add refine module:

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

Demo

First, you need to download YOLOv3 and HRNet pretrained models here and put it in the './demo/lib/checkpoint' directory. Then, you need to put your in-the-wild videos in the './demo/video/' directory.

Run the command below:

python demo/vis.py --video sample_video.mp4

Sample demo output:

Citation

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

@article{li2023exploiting,
  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={2023},
  volume={25},
  pages={1282-1293},
}

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.