This is an official PyTorch implementation of Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition" in CVPR 2023.

• Download the raw data of NTU RGB+D and PKU-MMD.
• For NTU RGB+D dataset, preprocess data with tools/ntu_gendata.py. For PKU-MMD dataset, preprocess data with tools/pku_part1_gendata.py.
• Then downsample the data to 50 frames with feeder/preprocess_ntu.py and feeder/preprocess_pku.py.
• If you don't want to process the original data, download the file folder in Google Drive action_dataset or BaiduYun link action_dataset, code: 0211. NTU-120 is also provided: NTU-120-frame50.

# Install other python libraries
$ pip install -r requirements.txt

This work is a two-phase training, the first phase is trained using the AimCLR method, you can refer to the code of AimCLR.

The second phase of training builds on the pre-trained parameters of the first phase. Example for unsupervised pre-training of 3s-ActCLR. You can change some settings of .yaml files in config/ntu60/pretext folder.

# train on NTU RGB+D xview joint stream
$ CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nnodes=1 --nproc_per_node=4 main.py pretrain_actclr --config ./config/ntu60/pretext/pretext_actclr_xview_joint.yaml

The linear evaluation process can be executed by running the script located in the skeleton_recognition directory. Example for linear evaluation of 3s-ActCLR. You can change .yaml files in config/ntu60/linear_eval folder.

# Linear_eval on NTU RGB+D xview
$ python main.py linear_evaluation --config config/ntu60/linear_eval/linear_eval_actclr_xview_joint.yaml

Please cite our paper if you find this repository useful in your resesarch:

@inproceedings{lin2023actionlet,
  Title= {Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition},
  Author= {Lin, Lilang and Zhang, Jiahang and Liu, Jiaying},
  Booktitle= {CVPR},
  Year= {2023}
}

The framework of our code is extended from the following repositories. We sincerely thank the authors for releasing the codes.

• The framework of our code is based on AimCLR.
• The encoder is based on ST-GCN.

This project is licensed under the terms of the MIT license.