Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about 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. See more results on our project page: https://realfill.github.io

翻译：近年来，生成式图像技术的进展催生了能够在不明确区域生成高质量、合理图像内容的外补全与内补全模型，但这些模型生成的内容必然缺乏真实性，因为它们缺少对真实场景的充分上下文信息。本文提出RealFill，一种新颖的生成式图像补全方法，能够用本该存在的内容填充图像中的缺失区域。RealFill是一种生成式图像内补全模型，仅需利用场景的少量参考图像即可实现个性化。这些参考图像无需与目标图像对齐，且可在视角、光照条件、相机光圈或图像风格存在显著差异的情况下拍摄。经过个性化后，RealFill能够补全目标图像，生成在视觉上引人入胜且忠实于原始场景的内容。我们在涵盖多样且具有挑战性场景的新图像补全基准上评估RealFill，发现其性能远超现有方法。更多结果请参见项目页面：https://realfill.github.io