  In many video restoration/translation tasks, image processing operations are
na\"ively extended to the video domain by processing each frame independently,
disregarding the temporal connection of the video frames. This disregard for
the temporal connection often leads to severe temporal inconsistencies.
State-Of-The-Art (SOTA) techniques that address these inconsistencies rely on
the availability of unprocessed videos to implicitly siphon and utilize
consistent video dynamics to restore the temporal consistency of frame-wise
processed videos which often jeopardizes the translation effect. We propose a
general framework for this task that learns to infer and utilize 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 without requiring the raw videos at test time. The
proposed framework produces SOTA results on two benchmark datasets, DAVIS and
videvo.net, processed by numerous image processing applications. The code and
the trained models are available at
\url{https://github.com/MKashifAli/TARONVD}.
