Deep Video Harmonization with Color Mapping Consistency
This work addresses video harmonization for video editing applications, but it is incremental as it builds on existing spatial methods by focusing on temporal consistency.
The authors tackled the problem of video harmonization by constructing a new dataset (HYoutube) and proposing a framework based on color mapping consistency to ensure temporal coherence, achieving effectiveness as proven through extensive experiments.
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.