Image Outpainting and Harmonization using GANs

Abstract

Although the inherently ambiguous task of predicting what resides on the far side all four edges of a image has rarely been explored before, we have a tendency to demonstrate that GANs hold powerful potential in manufacturing reasonable extrapolations. Two outpainting ways square measure projected that aim to instigate this line of research: the primary approach uses a context encoder inspired by common inpainting architectures and paradigms, whereas the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the design of the original image.