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.
- 03/24/2022: Demo and in-the-wild inference code is released!
- 03/15/2022: Our method has been verified in self-supervised pre-training as a backbone network!
- Cuda 11.1
- Python 3.6
- Pytorch 1.7.1
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.npzThe pretrained model can be found in here, please download it and put in the './checkpoint' dictory.
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.pyAfter 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.mp4Sample demo output:
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},
}
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.