Awesome

SPGSN

[ECCV2022] The source codes of 'Skeleton-parted graph scattering networks for 3D human motion prediction'. ECCV 2022

Dependencies

Python 3.6

Pytorch 0.3.1.

progress 1.5

Training commands

python3 main_3d.py --data_dir "[Path To Your H36M data]/h3.6m/dataset/" --input_n 10 --output_n 10 --dct_n 15 --exp [where to save the log file]

python main_cmu_3d.py --data_dir_cmu "[Path To Your CMU data]/cmu_mocap/" --input_n 10 --output_n 25 --dct_n 30 --exp [where to save the log file]

python main_3dpw_3d.py --data_dir_3dpw "[Path To Your 3DPW data]/3DPW/sequenceFiles/" --input_n 10 --output_n 30 --dct_n 35 --exp [where to save the log file]

Citing

If you use our code, please cite our work

@inproceedings{li2022Skeleton, title={Skeleton-parted graph scattering networks for 3D human motion prediction}, author={Li, Maosen and Chen, Siheng and Zhang, Zijing and Xie, Lingxi and Tian, Qi and Zhang, Ya}, booktitle={ECCV}, year={2022} } This readme file is going to be further updated.