DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models

• URL: http://arxiv.org/abs/2501.18590v1
• Date: Thu, 30 Jan 2025 18:59:11 GMT
• Title: DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
• Authors: Ruofan Liang, Zan Gojcic, Huan Ling, Jacob Munkberg, Jon Hasselgren, Zhi-Hao Lin, Jun Gao, Alexander Keller, Nandita Vijaykumar, Sanja Fidler, Zian Wang,
• Abstract summary: We introduce DiffusionRenderer, a neural approach that addresses the dual problem of inverse and forward rendering. Our model enables practical applications from a single video input--including relighting, material editing, and realistic object insertion.
• Score: 83.28670336340608
• License:
• Abstract: 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.

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