Probabilistic Semantic Inpainting with Pixel Constrained CNNs

Code

• github.com/Schlumberger/pixel-constrained-cnn-pytorch
  OfficialIn paperpytorch★ 0
• github.com/Schlumberger/pixel-constrained-cnn-tf
  tf★ 0

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.

Reproductions