Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
Ruofan Liang, Zan Gojcic, Merlin Nimier-David, David Acuna, Nandita Vijaykumar, Sanja Fidler, Zian Wang*
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."
BibTeX
@inproceedings{eccv2024_photorealisticob,
title = {Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering},
author = {Ruofan Liang and Zan Gojcic and Merlin Nimier-David and David Acuna and Nandita Vijaykumar and Sanja Fidler and Zian Wang*},
booktitle = {ECCV 2024},
year = {2024}
}