Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering

• URL: http://arxiv.org/abs/2408.09702v1
• Date: Mon, 19 Aug 2024 05:15:45 GMT
• Title: Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
• Authors: Ruofan Liang, Zan Gojcic, Merlin Nimier-David, David Acuna, Nandita Vijaykumar, Sanja Fidler, Zian Wang,
• Abstract summary: correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes.
• Score: 56.68286440268329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently "understand" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.

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