IntrinSync: Joint Intrinsic Decomposition and Reciprocal Rendering

Key points

• Inverse rendering decomposes an image into intrinsic properties such as appearance, illumination, geometry, and material, yet these properties are inherently interdependent.1
• At the channel level, we jointly decompose an input RGB into albedo, shading, surface normal, roughness, and metallic maps through a 1-to-N mapping, enabling information exchange across channels throughout generation.1
• Experiments on three datasets demonstrate that our method achieves competitive intrinsic estimation and forward rendering performance, improving coherence and physical consistency.1
• Beyond decomposition, IntrinSync provides a physically grounded interface for image editing, allowing intrinsic properties to be explicitly manipulated and rendered back into RGB images.1

Sources (1)

• [1]IntrinSync: Joint Intrinsic Decomposition and Reciprocal Rendering
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:05 AM

  In this paper, we introduce IntrinSync, a unified framework that captures this interdependence through joint-channel modeling and reciprocal inverse-forward rendering.

  Inverse rendering decomposes an image into intrinsic properties such as appearance, illumination, geometry, and material, yet these properties are inherently interdependent.

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