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Existing inpainting methods have achieved promising performance in recoveringdefected images of specific scenes. However, filling holes involving multiplesemantic categories remains challenging due to the obscure semantic boundariesand the mixture of different semantic textures. In this paper, we introducecoherence priors between the semantics and textures which make it possible toconcentrate on completing separate textures in a semantic-wise manner.Specifically, we adopt a multi-scale joint optimization framework to firstmodel the coherence priors and then accordingly interleavingly optimize imageinpainting and semantic segmentation in a coarse-to-fine manner. ASemantic-Wise Attention Propagation (SWAP) module is devised to refinecompleted image textures across scales by exploring non-local semanticcoherence, which effectively mitigates mix-up of textures. We also propose twocoherence losses to constrain the consistency between the semantics and theinpainted image in terms of the overall structure and detailed textures.Experimental results demonstrate the superiority of our proposed method forchallenging cases with complex holes.

Image Inpainting Guided by Coherence Priors of Semantics and Textures