Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
Authors: name: Ruofan Liang, name: Zan Gojcic, name: Merlin Nimier-David, name: David Acuna, name: Nandita Vijaykumar, name: Sanja Fidler, name: Zian Wang

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.