  Image inpainting is the task of plausibly restoring missing pixels within a
hole region that is to be removed from a target image. Most existing
technologies exploit patch similarities within the image, or leverage
large-scale training data to fill the hole using learned semantic and texture
information. However, due to the ill-posed nature of the inpainting task, such
methods struggle to complete larger holes containing complicated scenes. In
this paper, we propose TransFill, a multi-homography transformed fusion method
to fill the hole by referring to another source image that shares scene
contents with the target image. We first align the source image to the target
image by estimating multiple homographies guided by different depth levels. We
then learn to adjust the color and apply a pixel-level warping to each
homography-warped source image to make it more consistent with the target.
Finally, a pixel-level fusion module is learned to selectively merge the
different proposals. Our method achieves state-of-the-art performance on pairs
of images across a variety of wide baselines and color differences, and
generalizes to user-provided image pairs.
