: Given an arbitrary incomplete scene (left image), the proposed context encoder produces a plausible structural completion (middle image), which can be subsequently refined with a patch-based inpainting method (right image). Scene-agnostic visual inpainting remains very challenging despite progress in patch-based methods. Recently, Pathak et al. [26] 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 [32] with neural patches, our context encoder opens up new opportunities for prior-free visual inpainting.
Structural inpainting REF , which is CE based method, used an additional loss term called perceptual reconstruction loss for image inpainting.
Structural inpainting
MM '18
Computer Science