StructureFlow: Image Inpainting via Structure-aware Appearance Flow

Abstract

Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network.

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Cited by in corpus (5)

• Image Inpainting via Conditional Texture and Structure Dual Generation
• PISE: Person Image Synthesis and Editing with Decoupled GAN
• Parametric Reshaping of Portraits in Videos
• Bringing Old Photos Back to Life
• PD-GAN: Probabilistic Diverse GAN for Image Inpainting