论文标题
自然视频动力学的任务不可替代的恢复
Task Agnostic Restoration of Natural Video Dynamics
论文作者
论文摘要
在许多视频修复/翻译任务中，图像处理操作通过独立处理每个框架而无视视频框架的时间连接来幼稚地扩展到视频域。这种无视时间连接通常会导致严重的时间不一致。解决这些不一致之处的最先进的技术取决于未经处理的视频的可用性来隐式虹吸并利用一致的视频动力学来恢复框架处理的视频的时间一致性，从而危及翻译效果。我们为这项任务提供了一个通用框架，该框架学会了从不一致的视频中推断和利用一致的运动动态，以减轻时间闪烁，同时保留时间相邻和相对遥远的框架的感知质量，而无需在测试时需要原始视频。所提出的框架在两个基准数据集（Davis and Videvo.net）上产生SOTA结果，该数据集由许多图像处理应用程序处理。代码和训练有素的模型可在\ url {https://github.com/mkashifali/taronvd}上获得。
In many video restoration/translation tasks, image processing operations are naïvely 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}.
