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title
RealFill: Reference-Driven Generation for Authentic Image Completion
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RealFill: Reference-Driven Generation for Authentic Image Completion
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2026-02-19 09:22:20
raw text
RealFill RealFill Reference-Driven Generation for Authentic Image Completion Luming Tang 1,2 , Nataniel Ruiz 1 , Qinghao Chu 1 , Yuanzhen Li 1 , Aleksander Holynski 1 , David E. Jacobs 1 , Bharath Hariharan 2 , Yael Pritch 1 , Neal Wadhwa 1 , Kfir Aberman 1 , Michael Rubinstein 1 1 Google Research, 2 Cornell University SIGGRAPH 2024 (Journal Track) arXiv Data RealFill is able to complete the image with what should have been there . Abstract 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...
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