Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks

This is the code for the paper ICCV 2019 Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks in Pytorch.

Dependencies

cuda 9.0
Python 3.6
Pytorch 0.4.1.

Dataset setup

CPN 2D detections for Human3.6 M datasets are provided by VideoPose3D by Pavllo etal., which can be downloaded by:

cd data
wget https://dl.fbaipublicfiles.com/video-pose-3d/data_2d_h36m_cpn_ft_h36m_dbb.npz
wget https://dl.fbaipublicfiles.com/video-pose-3d/data_2d_h36m_detectron_ft_h36m.npz
cd ..

3D labels and ground truth can be downloaded and put in data/ folder 3d gt labels

Download pretrained model

Pretrained models can be found in pretrained models, pls download it and put in the ckpt/ dictory(create it if it does not exist)

Test the Model

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

python main_graph.py --pad 0 --show_protocol2 --post_refine --stgcn_reload 1 --post_refine_reload 1 --previous_dir '/ckpt/1_frame/cpn/' --stgcn_model 'model_st_gcn_36_eva_post_5062.pth' --post_refine_model 'model_post_refine_36_eva_post_5062.pth'

To test on Human3.6M on 3-frames, run:

python main_graph.py --pad 1 --show_protocol2 --post_refine --stgcn_reload 1 --post_refine_reload 1 --previous_dir '/ckpt/3_frame/cpn/' --stgcn_model 'model_st_gcn_58_eva_post_4903.pth' --post_refine_model 'model_post_refine_58_eva_post_4903.pth'

Train the Model

To train on Human3.6M with 3-frame, run:

python main_graph.py --pad 1 --pro_train 1 --save_model 1

After training for several epoches, add post_refine part

python main_graph.py --pad 1 --pro_train 1 --post_refine --save_model 1 --learning_rate 1e-5 --sym_penalty 1 --co_diff 1  --stgcn_reload 1  --previous_dir [your model saved path] --stgcn_model [your pretrained model]

Citation

@inproceedings{cai2019exploiting,
  title={Exploiting spatial-temporal relationships for 3d pose estimation via graph convolutional networks},
  author={Cai, Yujun and Ge, Liuhao and Liu, Jun and Cai, Jianfei and Cham, Tat-Jen and Yuan, Junsong and Thalmann, Nadia Magnenat},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={2272--2281},
  year={2019}
}

Acknowledgements

Some of our implementation code/preprocessed data was adapted from VideoPose3D by Pavllo et al., st-gcn by Yansijie et al., simple-yet-effective baseline by Julia et al.,Non-local neural networks. Thanks for their help!

Licence

MIT

vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks