  Scene-agnostic visual inpainting remains very challenging despite progress in
patch-based methods. Recently, Pathak et al. 2016 have introduced convolutional
"context encoders" (CEs) for unsupervised feature learning through image
completion tasks. With the additional help of adversarial training, CEs turned
out to be a promising tool to complete complex structures in real inpainting
problems. In the present paper we propose to push further this key ability by
relying on perceptual reconstruction losses at training time. We show on a wide
variety of visual scenes the merit of the approach for structural inpainting,
and confirm it through a user study. Combined with the optimization-based
refinement of Yang et al. 2016 with neural patches, our context encoder opens
up new opportunities for prior-free visual inpainting.
