Deep Video Harmonization With Color Mapping Consistency

Deep Video Harmonization With Color Mapping Consistency

Xinyuan Lu, Shengyuan Huang, Li Niu, Wenyan Cong, Liqing Zhang

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 1232-1238. https://doi.org/10.24963/ijcai.2022/172

Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the foreground of real videos to create synthetic composite videos. Moreover, we consider the temporal consistency in video harmonization task. Unlike previous works which establish the spatial correspondence, we design a novel framework based on the assumption of color mapping consistency, which leverages the color mapping of neighboring frames to refine the current frame. Extensive experiments on our HYouTube dataset prove the effectiveness of our proposed framework. Our dataset and code are available at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.
Keywords:
Computer Vision: Video analysis and understanding   
Computer Vision: Other