  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.
