Video inpainting, which aims at filling in missing regions of a video,
remains challenging due to the difficulty of preserving the precise spatial and
temporal coherence of video contents. In this work we propose a novel
flow-guided video inpainting approach. Rather than filling in the RGB pixels of
each frame directly, we consider video inpainting as a pixel propagation
problem. We first synthesize a spatially and temporally coherent optical flow
field across video frames using a newly designed Deep Flow Completion network.
Then the synthesized flow field is used to guide the propagation of pixels to
fill up the missing regions in the video. Specifically, the Deep Flow
Completion network follows a coarse-to-fine refinement to complete the flow
fields, while their quality is further improved by hard flow example mining.
Following the guide of the completed flow, the missing video regions can be
filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets
qualitatively and quantitatively, achieving the state-of-the-art performance in
terms of inpainting quality and speed.

本文提出了一种新颖的视频修复方法，使用 Deep Flow Completion 网络综合光流场来引导像素填充，从而保证了视频内容的时空连贯性，并在 DAVIS 和 YouTube-VOS 数据集上取得了最优的修复质量和速度。