融合物理模型与神经渲染，实现可随意光照的逼真发型建模
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
- 用扩展的头发材质模型捕捉主要光路，结合光照感知残差网络补全细节
- 在真实多视角发型数据上实现最佳的光照重演与视图合成效果
- 适合需要精细发型渲染与光照编辑的影视特效与虚拟人开发
我们提出 GroomLight，一种从多视角图像实现可重光照发型外观建模的新方法。现有发型捕获方法难以兼顾逼真渲染与光照重演能力。解析材质模型虽具物理基础，却常无法完全捕捉外观细节；而神经渲染方法虽擅长视图合成，但在新光照条件下泛化能力差。GroomLight 通过结合两种范式的优势，采用扩展的头发双向散射分布函数（BSDF）模型捕捉主要光传输，并引入光照感知残差模型重建剩余细节。我们进一步设计混合逆渲染流程，联合优化两个组件，实现高保真光照重演、视图合成与材质编辑。在真实世界发型数据上的大量评估表明，该方法达到当前最优性能。
原文摘要 · Abstract (English)
We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images. Existing hair capture methods struggle to balance photorealistic rendering with relighting capabilities. Analytical material models, while physically grounded, often fail to fully capture appearance details. Conversely, neural rendering approaches excel at view synthesis but generalize poorly to novel lighting conditions. GroomLight addresses this challenge by combining the strengths of both paradigms. It employs an extended hair BSDF model to capture primary light transport and a light-aware residual model to reconstruct the remaining details. We further propose a hybrid inverse rendering pipeline to optimize both components, enabling high-fidelity relighting, view synthesis, and material editing. Extensive evaluations on real-world hair data demonstrate state-of-the-art performance of our method.
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