In recent years, many deep learning techniques have been applied to the image inpainting
problem: the task of filling incomplete regions of an image. However, these models struggle …

Benefiting from powerful convolutional neural networks (CNNs), learning-based image
inpainting methods have made significant breakthroughs over the years. However, some …

Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …

We present a generative image inpainting system to complete images with free-form mask
and guidance. The system is based on gated convolutions learned from millions of images …

Image inpainting aims to fill missing regions of a damaged image with plausibly synthesized
content. Existing methods for image inpainting either fill the missing regions by borrowing …

Feature Normalization (FN) is an important technique to help neural network training, which
typically normalizes features across spatial dimensions. Most previous image inpainting …

Completing a corrupted image with correct structures and reasonable textures for a mixed
scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted …

Inpainting arbitrary missing regions is challenging because learning valid features for
various masked regions is nontrivial. Though U-shaped encoder-decoder frameworks have …

We study the task of image inpainting, which is to fill in the missing region of an incomplete
image with plausible contents. To this end, we propose a learning-based approach to …

Recently, learning-based algorithms for image inpainting achieve remarkable progress
dealing with squared or irregular holes. However, they fail to generate plausible textures …