VCNet: A Robust Approach to Blind Image Inpainting

Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image. Previous work assumes known missing-region-pattern, limiting the application scope. We instead relax the assumption by defining a new blind inpainting setting, making training a neural system robust against various unknown missing region patterns. Specifically, we propose a two-stage visual consistency network (VCN) to estimate where to fill (via masks) and generate what to fill. In this procedure, the unavoidable potential mask prediction errors lead to severe artifacts in the subsequent repairing. To address it, our VCN predicts semantically inconsistent regions first, making mask prediction more tractable. Then it repairs these estimated missing regions using a new spatial normalization, making VCN robust to mask prediction errors. Semantically convincing and visually compelling content can be generated. Extensive experiments show that our method is effective and robust in blind image inpainting. And our VCN allows for a wide spectrum of applications.