Instance-Aware Image Completion

Abstract

Image completion is a task that aims to fill in the missing region of a masked image with plausible contents. However, existing image completion methods tend to fill in the missing region with the surrounding texture instead of hallucinating a visual instance that is suitable in accordance with the context of the scene. In this work, we propose a novel image completion model, dubbed Refill, that hallucinates the missing instance that harmonizes well with - and thus preserves - the original context. Refill first adopts a transformer architecture that considers the types, locations of the visible instances, and the location of the missing region. Then, Refill completes the missing foreground and background semantic segmentation masks within the missing region, providing pixel-level semantic and structural guidance to generate missing contents with seamless boundaries. Finally, we condition the image synthesis blocks of Refill using the completed segmentation mask to generate photo-realistic contents to fill out the missing region. Experimental results show the superiority of Refill over state-of-the-art image completion approaches on various natural images.