Due to the scarcity of video processing methodologies, image processing operations are naively extended to the video domain by processing each frame independently. This disregard for the temporal connection in video processing often leads to severe temporal inconsistencies. State-of-the-art techniques that address these inconsistencies rely on the availability of unprocessed videos to siphon consistent video dynamics to restore the temporal consistency of frame-wise processed videos. We propose a novel general framework for this task that learns to infer consistent motion dynamics from inconsistent videos to mitigate the temporal flicker while preserving the perceptual quality for both the temporally neighboring and relatively distant frames. The proposed framework produces state-of-the-art results on two large-scale datasets, DAVIS and videvo.net, processed by numerous image processing tasks in a feed-forward manner. The code and the trained models will be released upon acceptance.

Learning Task Agnostic Temporal Consistency Correction