Image Outpainting and Harmonization using Generative Adversarial Networks

TL;DR

This paper explores the use of GANs for image outpainting and harmonization, proposing two methods that generate plausible extensions beyond image edges and integrate style consistency.

Contribution

It introduces two novel GAN-based outpainting techniques, including a context encoder and a post-processing step for style harmonization, advancing beyond traditional inpainting methods.

Findings

Effective extrapolation of image edges demonstrated

Enhanced style consistency in generated images

Supports arbitrary output resolutions

Abstract

Although the inherently ambiguous task of predicting what resides beyond all four edges of an image has rarely been explored before, we demonstrate that GANs hold powerful potential in producing reasonable extrapolations. Two outpainting methods are proposed that aim to instigate this line of research: the first approach uses a context encoder inspired by common inpainting architectures and paradigms, while the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the style of the original image, in an attempt to further boost the quality of the result and possibly allow for arbitrary output resolutions to be supported.