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Recording fast motion in a high FPS (frame-per-second) requires expensivehigh-speed cameras. As an alternative, interpolating low-FPS videos fromcommodity cameras has attracted significant attention. If only low-FPS videosare available, motion assumptions (linear or quadratic) are necessary to inferintermediate frames, which fail to model complex motions. Event camera, a newcamera with pixels producing events of brightness change at the temporalresolution of $\mu s$ $(10^{-6}$ second $)$, is a game-changing device toenable video interpolation at the presence of arbitrarily complex motion. Sinceevent camera is a novel sensor, its potential has not been fulfilled due to thelack of processing algorithms. The pioneering work Time Lens introduced eventcameras to video interpolation by designing optical devices to collect a largeamount of paired training data of high-speed frames and events, which is toocostly to scale. To fully unlock the potential of event cameras, this paperproposes a novel TimeReplayer algorithm to interpolate videos captured bycommodity cameras with events. It is trained in an unsupervisedcycle-consistent style, canceling the necessity of high-speed training data andbringing the additional ability of video extrapolation. Its state-of-the-artresults and demo videos in supplementary reveal the promising future ofevent-based vision.

TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation