  Image inpainting is a non-trivial task in computer vision due to multiple
possibilities for filling the missing data, which may be dependent on the
global information of the image. Most of the existing approaches use the
attention mechanism to learn the global context of the image. This attention
mechanism produces semantically plausible but blurry results because of
incapability to capture the global context. In this paper, we introduce
hypergraph convolution on spatial features to learn the complex relationship
among the data. We introduce a trainable mechanism to connect nodes using
hyperedges for hypergraph convolution. To the best of our knowledge, hypergraph
convolution have never been used on spatial features for any image-to-image
tasks in computer vision. Further, we introduce gated convolution in the
discriminator to enforce local consistency in the predicted image. The
experiments on Places2, CelebA-HQ, Paris Street View, and Facades datasets,
show that our approach achieves state-of-the-art results.
