  Existing learning-based image inpainting methods are still in challenge when
facing complex semantic environments and diverse hole patterns. The prior
information learned from the large scale training data is still insufficient
for these situations. Reference images captured covering the same scenes share
similar texture and structure priors with the corrupted images, which offers
new prospects for the image inpainting tasks. Inspired by this, we first build
a benchmark dataset containing 10K pairs of input and reference images for
reference-guided inpainting. Then we adopt an encoder-decoder structure to
separately infer the texture and structure features of the input image
considering their pattern discrepancy of texture and structure during
inpainting. A feature alignment module is further designed to refine these
features of the input image with the guidance of a reference image. Both
quantitative and qualitative evaluations demonstrate the superiority of our
method over the state-of-the-art methods in terms of completing complex holes.
