@inproceedings{
li2024harmony,
title={Harmony Everything! Masked Autoencoders for Video Harmonization},
author={Yuhang Li and Jincen Jiang and Xiaosong Yang and Youdong Ding and Jian Jun Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=C4cxNF0Yxq}
}
Video harmonization aims to address the discrepancy in color and lighting between foreground and background elements within video compositions, thereby enhancing the innate coherence of composite video content. Nevertheless, existing methods struggle to effectively handle video composite tasks with excessively large-scale foregrounds. In this paper, we propose Video Harmonization Masked Autoencoders (VHMAE), a simple yet powerful end-to-end video harmonization method designed to tackle this challenge once and for all. Unlike other typically MAE-based methods employing random or tube masking strategies, we innovative treat all foregrounds in each frame required for harmonization as prediction regions, which are designated as masked tokens and fed into our network to produce the final refinement video. To this end, the network is optimized to prioritize the harmonization task, proficiently reconstructing the masked region despite the limited background information. Specifically, we introduce the Pattern Alignment Module (PAM) to extract content information from the extensive masked foreground region, aligning the latent semantic features of the masked foreground content with the background context while disregarding the impact of various colors or illumination. Moreover, We propose the Patch Balancing Loss, which effectively mitigates the undesirable grid-like artifacts commonly observed in MAE-based approaches for image generation, thereby ensuring consistency between the predicted foreground and the visible background. Additionally, we introduce a real-composited video harmonization dataset named RCVH, which serves as a valuable benchmark for assessing the efficacy of techniques aimed at video harmonization across different real video sources. Comprehensive experiments demonstrate that our VHMAE outperforms state-of-the-art techniques on both our RCVH and the publicly available HYouTube dataset.
Harmony Everything! Masked Autoencoders for Video Harmonization
Yuhang Li Jincen Jiang Xiaosong Yang Youdong Ding Jian Jun Zhang