Robust Image Outpainting With Learnable Image Margins

Abstract

Given a partial image input, image outpainting is to produce the desirable output by recovering or extending the surrounding image regions. While existing image outpainting methods achieve impressive results based on the recent advances of deep learning, they either lack the ability to extend image regions in arbitrary directions or require the filling image margins to be given in advance. To address this challenging task, we propose a unique deep learning framework for robust image outpainting, which consists of a margin prediction network and a teacher-student-based network for producing outpainted images. Our proposed model does not require image filling margins to be known beforehand, while both image appearance and perceptual feature consistencies can be jointly enforced. Our experiments quantitatively and qualitatively verify the effectiveness of our method, which is shown to perform favorably against baseline and state-of-the-art image outpainting works.