Segment Anything Model Meets Image Harmonization
Haoxing Chen Yaohui Li Zhangxuan Gu Zhuoer Xu Jun Lan Huaxiong Li
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.