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

翻译：近期生成式图像领域的进展催生了高质量、合理内容的图像外补全与内补全模型。然而，这些模型所生成的内容必然缺乏真实性，因为它们对真实场景缺乏感知。本文提出RealFill——一种新颖的图像补全生成方法，能够用场景中理应存在的内容填补图像的缺失区域。RealFill是一种生成式内补全模型，仅需少量场景参考图像即可实现个性化定制。这些参考图像无需与目标图像对齐，可在视角、光照条件、相机光圈或图像风格差异极大的情况下拍摄。经过个性化定制后，RealFill能够生成与原始场景高度一致的视觉内容，完成目标图像的补全。我们在覆盖多样化挑战场景的新型图像补全基准上评估了RealFill，发现其性能大幅超越现有方法。项目主页：https://realfill.github.io