Video Harmonization with Triplet Spatio-Temporal Variation Patterns

Zonghui Guo, Xinyu Han, Jie Zhang, Shiguang Shan, Haiyong Zheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19177-19186


Video harmonization is an important and challenging task that aims to obtain visually realistic composite videos by automatically adjusting the foreground's appearance to harmonize with the background. Inspired by the short-term and long-term gradual adjustment process of manual harmonization we present a Video Triplet Transformer framework to model three spatio-temporal variation patterns within videos i.e. short-term spatial as well as long-term global and dynamic for video-to-video tasks like video harmonization. Specifically for short-term harmonization we adjust foreground appearance to consist with background in spatial dimension based on the neighbor frames; for long-term harmonization we not only explore global appearance variations to enhance temporal consistency but also alleviate motion offset constraints to align similar contextual appearances dynamically. Extensive experiments and ablation studies demonstrate the effectiveness of our method achieving state-of-the-art performance in video harmonization video enhancement and video demoireing tasks. We also propose a temporal consistency metric to better evaluate the harmonized videos. Code is available at

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@InProceedings{Guo_2024_CVPR, author = {Guo, Zonghui and Han, Xinyu and Zhang, Jie and Shan, Shiguang and Zheng, Haiyong}, title = {Video Harmonization with Triplet Spatio-Temporal Variation Patterns}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {19177-19186} }