Probabilistic Semantic Inpainting with Pixel Constrained CNNs

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Emilien Dupont, Suhas Suresha ;
Proceedings of Machine Learning Research, PMLR 89:2261-2270, 2019.

Abstract

Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are deterministic: given an image with missing regions, a single inpainted image is generated. However, there are often several plausible inpaintings for a given missing region. In this paper, we propose a method to perform probabilistic semantic inpainting by building a model, based on PixelCNNs, that learns a distribution of images conditioned on a subset of visible pixels. Experiments on the MNIST and CelebA datasets show that our method produces diverse and realistic inpaintings.

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