  Deep generative models have shown success in automatically synthesizing
missing image regions using surrounding context. However, users cannot directly
decide what content to synthesize with such approaches. We propose an
end-to-end network for image inpainting that uses a different image to guide
the synthesis of new content to fill the hole. A key challenge addressed by our
approach is synthesizing new content in regions where the guidance image and
the context of the original image are inconsistent. We conduct four studies
that demonstrate our results yield more realistic image inpainting results over
seven baselines.
