发表机构
Shenzhen University; Tencent; Hebrew University of Jerusalem; Tel Aviv University（深圳大学; 腾讯; 耶路撒冷希伯来大学; 特拉维夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
IntrinSync是联合内在分解与互渲染的统一框架，通过联合通道建模与双循环一致性目标提升内在估计与前向渲染的物理一致性，还可用于图像编辑。
AI 中文摘要
逆渲染将图像分解为外观、光照、几何、材质等内在属性，但这些属性本质上相互依赖。可靠的分解应生成的内在图不仅各自合理，还需在解释图像时相互兼容。然而现有方法要么孤立建模内在通道，要么将逆渲染与前向渲染视为独立过程，未充分利用这种相互依赖性。本文提出IntrinSync，这一统一框架通过联合通道建模与逆-前向互渲染捕捉该相互依赖性：在通道层面，通过1对N映射将输入RGB联合分解为反照率、着色、表面法向、粗糙度和金属度图，使生成过程中各通道间实现信息交换；在过程层面，通过双循环一致性目标建立逆-前向互性，对齐闭环内对应预测结果。在三个数据集上的实验表明，本文方法在内在估计与前向渲染任务上取得了竞争力的性能，提升了一致性与物理一致性。除分解外，IntrinSync还为图像编辑提供了基于物理的接口，允许显式操纵内在属性并将其重新渲染为RGB图像。
英文摘要
Inverse rendering decomposes an image into intrinsic properties such as appearance, illumination, geometry, and material, yet these properties are inherently interdependent. A reliable decomposition should produce intrinsic maps that are not only individually plausible, but also mutually compatible in explaining the image. However, existing methods either model intrinsic channels in isolation or treat inverse and forward rendering as separate processes, leaving the interdependence underexploited. In this paper, we introduce IntrinSync, a unified framework that captures this interdependence through joint-channel modeling and reciprocal inverse-forward rendering. 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. At the process level, we establish inverse-forward reciprocity through a dual cycle-consistent objective that aligns corresponding predictions across a closed loop. Experiments on three datasets demonstrate that our method achieves competitive intrinsic estimation and forward rendering performance, improving coherence and physical consistency. Beyond decomposition, IntrinSync provides a physically grounded interface for image editing, allowing intrinsic properties to be explicitly manipulated and rendered back into RGB images.
Comments21 pages
