- gendata/
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...

 cd ./gendata/ntu # or cd ./gendata/ntu120
 # Get skeleton of each performer
 python3 get_raw_skes_data.py
 # Remove the bad skeleton 
 python3 get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python3 seq_transformation.py

# Example: training STTFormer on NTU RGB+D 60 cross subject under bone modality
python3 main.py --config config/ntu60_xsub_bone.yaml

# Example: testing STTFormer on NTU RGB+D 60 cross subject under bone modality
python3 main.py --config config/ntu60_xsub_bone.yaml --run_mode test --save_score True --weights work_dir/ntu60/xsub_bone/xsub_bone.pt

# Example: ensemble three modalities of STTFormer on NTU RGB+D 60 cross subject
python3 ensemble.py --dataset ntu/xsub --joint_dir work_dir/ntu60/xsub_joint --bone_dir work_dir/ntu60/xsub_bone --joint_motion_dir work_dir/ntu60/xsub_joint_motion

  @article{Qiu2022SpatioTemporalTT,
    title={Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition},
    author={Helei Qiu and Biao Hou and Bo Ren and Xiaohua Zhang},
    journal={ArXiv},
    year={2022},
    volume={abs/2201.02849}
  }