Deep Video Harmonization With Color Mapping Consistency
Xinyuan Lu, Shengyuan Huang, Li Niu, Wenyan Cong, Liqing Zhang
Abstract
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.
BibTeX
@inproceedings{ijcai2022p172,
title = {Deep Video Harmonization With Color Mapping Consistency},
author = {Lu, Xinyuan and Huang, Shengyuan and Niu, Li and Cong, Wenyan and Zhang, Liqing},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {1232--1238},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/172},
url = {https://doi.org/10.24963/ijcai.2022/172},
}