Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurately simulates the light transport, but relies on precise scene representations--explicit 3D geometry, high-quality material properties, and lighting conditions--that are often impractical to obtain in real-world scenarios. Therefore, we introduce DiffusionRenderer, a neural approach that addresses the dual problem of inverse and forward rendering within a holistic framework. Leveraging powerful video diffusion model priors, the inverse rendering model accurately estimates G-buffers from real-world videos, providing an interface for image editing tasks, and training data for the rendering model. Conversely, our rendering model generates photorealistic images from G-buffers without explicit light transport simulation. Experiments demonstrate that DiffusionRenderer effectively approximates inverse and forwards rendering, consistently outperforming the state-of-the-art. Our model enables practical applications from a single video input--including relighting, material editing, and realistic object insertion.
@article{arxiv.2501.18590,
title = {DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models},
author = {Ruofan Liang and Zan Gojcic and Huan Ling and Jacob Munkberg and Jon Hasselgren and Zhi-Hao Lin and Jun Gao and Alexander Keller and Nandita Vijaykumar and Sanja Fidler and Zian Wang},
journal= {arXiv preprint arXiv:2501.18590},
year = {2025}
}