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The dataset contains 2D pose annotations for the Micro-Action 52 dataset.

The data format follows the skeleton annotation structure defined by MMAction2

The 2D keypoints were extracted using OpenPose, including a total of 28 keypints, consisting of:

  • 18 body keypoints
  • 5 left-hand fingertip keypoints
  • 5 right-hand fingertip keypoints

The connections between keypoints (i.e., the skeleton graph) are defined as follows:

skeletons = ((4, 3), (3, 2), (7, 6), (6, 5), (13, 12), (12, 11), (10, 9),
             (9, 8), (11, 5), (8, 2), (5, 1), (2, 1), (0, 1), (15, 0),
             (14, 0), (17, 15), (16, 14),
             (23, 4), (24, 4), (25, 4), (26, 4), (27, 4),
             (18, 7), (19, 7), (20, 7), (21, 7), (22, 7))

arXiv version

If you find this dataset useful in your research, please consider citing the following works:

@inproceedings{li2025prototypical,
  title={Prototypical calibrating ambiguous samples for micro-action recognition},
  author={Li, Kun and Guo, Dan and Chen, Guoliang and Fan, Chunxiao and Xu, Jingyuan and Wu, Zhiliang and Fan, Hehe and Wang, Meng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={5},
  pages={4815--4823},
  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}
}

If you have any questions or feedback, please contact us at [[email protected]].

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