Video inpainting aims to fill spatio-temporal holes with plausible content in
a video. Despite tremendous progress of deep neural networks for image
inpainting, it is challenging to extend these methods to the video domain due
to the additional time dimension. In this work, we propose a novel deep network
architecture for fast video inpainting. Built upon an image-based
encoder-decoder model, our framework is designed to collect and refine
information from neighbor frames and synthesize still-unknown regions. At the
same time, the output is enforced to be temporally consistent by a recurrent
feedback and a temporal memory module. Compared with the state-of-the-art image
inpainting algorithm, our method produces videos that are much more
semantically correct and temporally smooth. In contrast to the prior video
completion method which relies on time-consuming optimization, our method runs
in near real-time while generating competitive video results. Finally, we
applied our framework to video retargeting task, and obtain visually pleasing
results.

本文提出了一种基于深度学习的快速视频修复网络，结合图像编解码模型，利用邻帧信息综合填充未知视频区域，并运用循环反馈和时间存储模块确保输出的时空连续性，与之前的视频修复方法相比，其结果更为语义正确和时空平滑，且可用于视频重定向任务并生成优质视觉效果。