StructureFlow: Image Inpainting via Structure-Aware Appearance Flow
2019 IEEE/CVF International Conference on Computer Vision (ICCV)(2019)
摘要
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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关键词
edge-preserved smooth images,structure reconstructor,missing structures,reconstructed structures,texture generator,image details,StructureFlow,structure-aware appearance flow,image inpainting techniques,deep neural networks,reasonable structures,fine-grained textures,two-stage model,inpainting task,structure reconstruction,texture generation
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