Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
This repo is the official implementation of Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation in Pytorch.
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{li2021exploiting,
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={arXiv preprint arXiv:2103.14304},
year={2021}
}
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.
