  We propose the onion-peel networks for video completion. Given a set of
reference images and a target image with holes, our network fills the hole by
referring the contents in the reference images. Our onion-peel network
progressively fills the hole from the hole boundary enabling it to exploit
richer contextual information for the missing regions every step. Given a
sufficient number of recurrences, even a large hole can be inpainted
successfully. To attend to the missing information visible in the reference
images, we propose an asymmetric attention block that computes similarities
between the hole boundary pixels in the target and the non-hole pixels in the
references in a non-local manner. With our attention block, our network can
have an unlimited spatial-temporal window size and fill the holes with globally
coherent contents. In addition, our framework is applicable to the image
completion guided by the reference images without any modification, which is
difficult to do with the previous methods. We validate that our method produces
visually pleasing image and video inpainting results in realistic test cases.
