  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.
