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EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera

MPS-Authors
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Xu,  Lan
Computer Graphics, MPI for Informatics, Max Planck Society;

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Xu,  Weipeng
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons239654

Golyanik,  Vladislav
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons101676

Habermann,  Marc
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1908.11505.pdf
(Preprint), 4MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Xu, L., Xu, W., Golyanik, V., Habermann, M., Fang, L., & Theobalt, C. (2019). EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera. doi:Xu_arXiv1908.11505.


Cite as: http://hdl.handle.net/21.11116/0000-0005-7D7B-6
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.