  Blind inpainting is a task to automatically complete visual contents without
specifying masks for missing areas in an image. Previous works assume missing
region patterns are known, limiting its application scope. In this paper, we
relax the assumption by defining a new blind inpainting setting, making
training a blind inpainting neural system robust against various unknown
missing region patterns. Specifically, we propose a two-stage visual
consistency network (VCN), meant 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, enabling VCN to be robust to the mask
prediction errors. In this way, semantically convincing and visually compelling
content is thus generated. Extensive experiments are conducted, showing our
method is effective and robust in blind image inpainting. And our VCN allows
for a wide spectrum of applications.
