DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models

📝 Original Info

• Title: DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
• ArXiv ID: 2501.18590
• Date: 2025-01-30
• Authors: 정보 없음 (제공된 텍스트에 저자 정보가 포함되어 있지 않습니다.)

📝 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.

💡 Deep Analysis

📄 Full Content

Reference