Shift-GCN

The implementation for "Skeleton-Based Action Recognition with Shift Graph Convolutional Network" (CVPR2020 oral). Shift-GCN is a lightweight skeleton-based action recognition model, which exceeds state-of-the-art methods with 10x less FLOPs.

Prerequisite

• PyTorch 0.4.1
• Cuda 9.0
• g++ 5.4.0

Compile cuda extensions

cd ./model/Temporal_shift
bash run.sh

Data Preparation

• Download the raw data of NTU-RGBD and NTU-RGBD120. Put NTU-RGBD data under the directory ./data/nturgbd_raw. Put NTU-RGBD120 data under the directory ./data/nturgbd120_raw.

• For NTU-RGBD, preprocess data with python data_gen/ntu_gendata.py. For NTU-RGBD120, preprocess data with python data_gen/ntu120_gendata.py.

• Generate the bone data with python data_gen/gen_bone_data.py.

• Generate the motion data with python data_gen/gen_motion_data.py.

Training & Testing

• NTU X-view

  python main.py --config ./config/nturgbd-cross-view/train_joint.yaml

  python main.py --config ./config/nturgbd-cross-view/train_bone.yaml

  python main.py --config ./config/nturgbd-cross-view/train_joint_motion.yaml

  python main.py --config ./config/nturgbd-cross-view/train_bone_motion.yaml

• NTU X-sub

  python main.py --config ./config/nturgbd-cross-subject/train_joint.yaml

  python main.py --config ./config/nturgbd-cross-subject/train_bone.yaml

  python main.py --config ./config/nturgbd-cross-subject/train_joint_motion.yaml

  python main.py --config ./config/nturgbd-cross-subject/train_bone_motion.yaml

• For NTU120, change the dataset path in config files, and change num_class in config files from 60 to 120.

Multi-stream ensemble

To ensemble the results of 4 streams. Change models name in ensemble.py depending on your experiment setting. Then run python ensemble.py.

Trained models

We release several trained models:

Model	Dataset	Setting	Top1(%)
./save_models/ntu_ShiftGCN_joint_xview.pt	NTU-RGBD	X-view	95.1
./save_models/ntu_ShiftGCN_joint_xsub.pt	NTU-RGBD	X-sub	87.8
./save_models/ntu120_ShiftGCN_joint_xsetup.pt	NTU-RGBD120	X-setup	83.2
./save_models/ntu120_ShiftGCN_joint_xsub.pt	NTU-RGBD120	X-sub	80.9

Citation

If you find this model useful for your research, please use the following BibTeX entry.

@inproceedings{cheng2020shiftgcn,  
  title     = {Skeleton-Based Action Recognition with Shift Graph Convolutional Network},  
  author    = {Ke Cheng and Yifan Zhang and Xiangyu He and Weihan Chen and Jian Cheng and Hanqing Lu},  
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},  
  year      = {2020},  
}