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:
- Email: [email protected] / [email protected]
- [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 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.
- [2025.02.11] EgoExo-Fitness dataset is available on Huggingface. Click here for more details.
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.
To download the dataset, please sign the License Agreement and send it to [email protected] for downloading our datasets and raw annotations. Click here to learn more details about the released dataset and the raw annotations.
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},
pages={363--382},
year={2024},
organization={Springer}
}