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arXiv · 2312.12729

Segment Anything Model Meets Image Harmonization

Haoxing Chen·Yaohui Li·Zhangxuan Gu·Zhuoer Xu·Jun Lan·Huaxiong Li
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Abstract

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.

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Haoxing Chen, Yaohui Li, Zhangxuan Gu, Zhuoer Xu, Jun Lan, Huaxiong Li. 2023-12-20. Segment Anything Model Meets Image Harmonization. https://arxiv.org/abs/2312.12729

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