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EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding

Yuanming Li, Weijin Huang, Anlan Wang, Lingan Zeng, Jingke Meng, Weishi Zheng

[Paper w/ Appendix]

Official repository of ECCV-2024 paper "EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding"

All resources will be available soon. If you have any question, please feel free to contact us.

Contact:

💬 News

  • [2024.06.14] The preprint paper is available.
  • [2024.07.02] This work is accepted by ECCV-2024. Many thanks to the co-workers!🥳🎉🎊
  • [2024.07.09] EgoExo-Fitness dataset and part of the raw annotations are open to apply for.🔥🔥🔥 Click here for more details.
  • [2024.07.09] Code for Cross-View Sequence Verification benchmark is available. Click here for more details.

📎 Abstract

We present EgoExo-Fitness, a new full-body action understanding dataset, featuring fitness sequence videos recorded from synchronized egocentric and fixed exocentric (third-person) cameras. Compared with existing full-body action understanding datasets, EgoExo-Fitness not only contains videos from first-person perspectives, but also provides rich annotations. Specifically, two-level temporal boundaries are provided to localize single action videos along with sub-steps of each action. More importantly, EgoExo-Fitness introduces innovative annotations for interpretable action judgement--including technical keypoint verification, natural language comments on action execution, and action quality scores. Combining all of these, EgoExo-Fitness provides new resources to study egocentric and exocentric full-body action understanding across dimensions of what, when, and how well. To facilitate research on egocentric and exocentric full-body action understanding, we construct benchmarks on a suite of tasks (\ie, action recognition, action localization, cross-view sequence verification, cross-view skill determination, and a newly proposed task of guidance-based execution verification), together with detailed analysis.

⏬ Download

To download the dataset, please sign the License Agreement and send it to [email protected] for downloading our datasets and raw annotations. The shared link will be expired in one week. Click here to learn more details about the released dataset and the raw annotations.

📑 Citation

Please cite it if you find this work useful.

@inproceedings{li2024egoexo,
  title={EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding},
  author={Li, Yuan-Ming and Huang, Wei-Jin and Wang, An-Lan and Zeng, Ling-An and Meng, Jing-Ke and Zheng, Wei-Shi},
  booktitle={European Conference on Computer Vision},
  year={2024}
}