**Skeleton Data for Micro-Action 52 dataset**

<img src="https://cdn-uploads.huggingface.co/production/uploads/6715123c301fc44e100a4295/Lebp-uITidE7Y81e8-n_O.png" alt="image/png" style="zoom: 25%;" />


## Introduction

This repository is designed specifically for Skeleton-based Micro-Action Recognition research. 

The Micro-Action-52 (MA-52) dataset is only to be used for **non-commercial scientific purposes**. 

Please note that the test set is withheld for competition purposes. 

You can evaluate your results by following the provided [instructions](https://github.com/VUT-HFUT/Micro-Action/tree/main/mar_scripts#codabench-submission-test-set).


## Citation
Please consider citing the related paper in your publications if it helps your research.


```
@article{gu2025motion,
  title={Motion matters: Motion-guided modulation network for skeleton-based micro-action recognition},
  author={Gu, Jihao and Li, Kun and Wang, Fei and Wei, Yanyan and Wu, Zhiliang and Fan, Hehe and Wang, Meng},
  journal={arXiv preprint arXiv:2507.21977},
  year={2025}
}
@article{guo2024benchmarking,
  title={Benchmarking Micro-action Recognition: Dataset, Methods, and Applications},
  author={Guo, Dan and Li, Kun and Hu, Bin and Zhang, Yan and Wang, Meng},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2024},
  volume={34},
  number={7},
  pages={6238-6252},
  publisher={IEEE},
  doi={10.1109/TCSVT.2024.3358415}
}
@article{li2024mmad,
  title={Mmad: Multi-label micro-action detection in videos},
  author={Li, Kun and Liu, Pengyu and Guo, Dan and Wang, Fei and Wu, Zhiliang and Fan, Hehe and Wang, Meng},
  journal={arXiv preprint arXiv:2407.05311},
  year={2024}
}

```