RealFill: Reference-Driven Generation for Authentic Image Completion
Luming Tang Nataniel Ruiz Qinghao Chu Yuanzhen Li Aleksander Holynski David E. Jacobs Bharath Hariharan Yael Pritch Neal Wadhwa Kfir Aberman Michael Rubinstein
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