1. Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
- Author
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Liang, Ruofan, Gojcic, Zan, Nimier-David, Merlin, Acuna, David, Vijaykumar, Nandita, Fidler, Sanja, and Wang, Zian
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Graphics - 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., Comment: ECCV 2024, Project page: https://research.nvidia.com/labs/toronto-ai/DiPIR/
- Published
- 2024