EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera
Abstract
The high frame rate is a critical requirement for capturing fast human motions. In this setting, existing markerless image-based methods are constrained by the lighting requirement, the high data bandwidth and the consequent high computation overhead. In this paper, we propose EventCap -- the first approach for 3D capturing of high-speed human motions using a single event camera. Our method combines model-based optimization and CNN-based human pose detection to capture high frequency motion details and to reduce the drifting in the tracking. As a result, we can capture fast motions at millisecond resolution with significantly higher data efficiency than using high frame rate videos. Experiments on our new event-based fast human motion dataset demonstrate the effectiveness and accuracy of our method, as well as its robustness to challenging lighting conditions.
Cite
Text
Xu et al. "EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00502Markdown
[Xu et al. "EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/xu2020cvpr-eventcap/) doi:10.1109/CVPR42600.2020.00502BibTeX
@inproceedings{xu2020cvpr-eventcap,
title = {{EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera}},
author = {Xu, Lan and Xu, Weipeng and Golyanik, Vladislav and Habermann, Marc and Fang, Lu and Theobalt, Christian},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00502},
url = {https://mlanthology.org/cvpr/2020/xu2020cvpr-eventcap/}
}