Who Looks like Me: Semantic Routed Image Harmonization

Abstract

Image harmonization, aiming to seamlessly blend extraneous foreground objects with background images, is a promising and challenging task.Ensuring a synthetic image appears realistic requires maintaining consistency in visual characteristics, such as texture and style, across global and semantic regions.In this paper, We approach image harmonization as a semantic routed style transfer problem, and propose an imageharmonization model by routing semantic similarity explicitly to enhance the consistency of appearance characteristics.To refine calculate the similarity between the composed foreground and background instance, we propose an InstanceSimilarity Evaluation Module(ISEM).To harness analogous semantic information effectively, we further introduceStyle Transfer Block(STB) to establish fine-grained foreground-background semantic correlation.Our method has achieved excellent experimental results on existing datasets and our model outperforms the state-of-the-art by a margin of 0.45 dB on iHarmony4 dataset.