Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.00502 | 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lan Xu | 1 | 30 | 11.01 |
| WeiPeng Xu | 2 | 314 | 17.47 |
| Vladislav Golyanik | 3 | 22 | 12.55 |
| Marc Habermann | 4 | 30 | 4.49 |
| Lu Fang | 5 | 343 | 55.27 |
| Christian Theobalt | 6 | 3211 | 159.16 |