This repo is the official implementation of Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation in Pytorch.
- 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).
${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 Google_Drive, please download it and put in the './checkpoint' dictory.
To test on pretrained model on Human3.6M with 351-frames:
python main.py --frames 351 --refine --reload 1 --refine_reload 1 --previous_dir 'checkpoint/351'
To train on Human3.6M with 351-frame:
python main.py --frames 351 --train 1 \
After training for several epoches, add refine module
python main.py --frames 351 --train 1 --refine --lr 1e-5 --reload 1 --previous_dir [your model saved path] \
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}
}
Our code is built on top of ST-GCN and is extended from the following repositories. We thank the authors for releasing the codes.