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.
Our code is tested on Ubuntu 18 with Pytorch 1.7.1 and Python 3.9.
pip3 install -r requirements.txt
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
The pretrained model can be found in here, please download it and put in the './checkpoint/pretrained' directory.
To test on pretrained model on Human3.6M:
python main.py --test --refine --reload --refine_reload --previous_dir 'checkpoint/pretrained'
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]
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:
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},
}
Our code is built on top of ST-GCN and is extended from the following repositories. We thank the authors for releasing the codes.
This project is licensed under the terms of the MIT license.