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The high frame rate is a critical requirement for capturing fast humanmotions. In this setting, existing markerless image-based methods areconstrained by the lighting requirement, the high data bandwidth and theconsequent high computation overhead. In this paper, we propose EventCap ---the first approach for 3D capturing of high-speed human motions using a singleevent camera. Our method combines model-based optimization and CNN-based humanpose detection to capture high-frequency motion details and to reduce thedrifting in the tracking. As a result, we can capture fast motions atmillisecond resolution with significantly higher data efficiency than usinghigh frame rate videos. Experiments on our new event-based fast human motiondataset demonstrate the effectiveness and accuracy of our method, as well asits robustness to challenging lighting conditions.

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