100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement

摘要

Despite the higher video-completion quality that recently proposed methods have enabled for a wide variety of cases, their computational complexity remains a major concern. These methods typically frame video completion as an optimization problem over the whole spatiotemporal domain-a problem that is expensive to solve both in time and space. In this paper we propose a lighter-weight multipass video-completion pipeline that replaces global spatiotemporal optimization with simpler frame-by-frame motion reconstruction and refinement. We achieve a processing speed of 2.6 seconds per frame on Full HD content while delivering nearly state-of-the-art completion quality for a wide range of dynamic scenes captured using a free-moving camera. To validate the performance of our proposed method, we conducted a subjective comparison of different video-completion results for 26 test sequences from the DAVIS data set.