Deep Flow-Guided Video Inpainting

Abstract

Video inpainting, which aims at filling in missing regions in 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 the 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 use the synthesized flow fields to guide the propagation of pixels to fill up the missing regions in the video. Specifically, the Deep Flow Competion network follows a coarse-to-fine refinement strategy to complete the flow fields, while their quality is further improved by hard flow example mining. Following the guide of the completed flow fields, the missing video regions can be filled up precisely. Our method is evaluated on DAVIS and YouTubeVOS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of inpainting quality and speed.