Double-Chain Graph Convolution Transformer for 3D Human Pose Estimation

Double-Chain Graph Convolution Transformer for 3D Human Pose Estimation,
Hongbo Kang, Yong Wang, Mengyuan Liu, Doudou Wu, Peng Liu, Wenming Yang
TMM, 2025

This link contains the code that supports our latest work: DRPose

Results on Human3.6M

Protocol 1 (mean per-joint position error) when 2D keypoints detected by CPN, HRNet and the ground truth of 2D poses.

Dependencies

• Python 3.7+
• PyTorch >= 1.10.0

pip install -r requirement.txt

Dataset setup

Please download the dataset here 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

Download pretrained model

The pretrained model is here, please download it and put it in the './ckpt/pretrained' directory.

Test the model

To test on Human3.6M on single frame, run:

python main.py --reload --previous_dir "ckpt/pretrained" 

Train the model

To train on Human3.6M with single frame, run:

python main.py --train -n 'name'

Demo

To begin, download the YOLOv3 and HRNet pretrained models here and put it in the './demo/lib/checkpoint' directory. Next, download the pretrained model and put it in the './ckpt/pretrained' directory. Lastly, Put your own images in the './demo/figure', and run:

python demo/vis.py

Citation

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

@article{kang2025double,
  title={Double-Chain Graph Convolution Transformer for 3D Human Pose Estimation},
  author={Kang, Hongbo and Wang, Yong and Liu, Mengyuan and Wu, Doudou and Liu, Peng and Yang, Wenming},
  journal={IEEE Transactions on Multimedia},
  year={2025},
  publisher={IEEE}
}

Acknowledgement

Our code is extended from the following repositories. We thank the authors for releasing the codes.

• ST-GCN
• MHFormer
• VideoPose3D
• StridedTransformer-Pose3D
• HTNet