RealFill: Reference-Driven Generation for Authentic Image Completion
It addresses the issue of producing authentic image completions for users in computer vision, though it is incremental as it builds on existing generative models.
The paper tackles the problem of generating inauthentic content in image completion by proposing RealFill, a generative inpainting model personalized with a few reference images of a scene, which outperforms existing approaches by a large margin on a new benchmark.
Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions. However, the content these models hallucinate is necessarily inauthentic, since they are unaware of the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. Project page: https://realfill.github.io