Image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images. Current methods adopt either global-level or pixel-level feature matching. Global-level feature matching ignores the proximity prior, treating foreground and background as separate entities. On the other hand, pixel-level feature matching loses contextual information. Therefore, it is necessary to use the information from semantic maps that describe different objects to guide harmonization. In this paper, we propose Semantic-guided Region-aware Instance Normalization (SRIN) that can utilize the semantic segmentation maps output by a pre-trained Segment Anything Model (SAM) to guide the visual consistency learning of foreground and background features. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods.
@article{arxiv.2312.12729,
title = {Segment Anything Model Meets Image Harmonization},
author = {Haoxing Chen and Yaohui Li and Zhangxuan Gu and Zhuoer Xu and Jun Lan and Huaxiong Li},
journal= {arXiv preprint arXiv:2312.12729},
year = {2023}
}