  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
