从RGB图像反推皮肤全谱散射参数，提升渲染真实感
Spectral Subsurface Scattering from RGB via Biophysical Skin Inversion
- 用混合介质模型模拟皮肤多层结构，基于RGB反推光学参数
- 训练神经网络从单张RGB图预测散射半径、各向异性等参数
- 可直接接入路径追踪器，适合影视级角色渲染场景
本文提出一种用于路径追踪渲染的皮肤光谱光学反演方法。皮肤是复杂的多层介质，其外观由生物物理色团混合决定。现有方法依赖介质均质化处理，通过反射纹理反演反照率，并手动调整散射距离和各向异性，导致创作过程高度依赖人工且散射分布不准确。为此，我们推广了现有反照率反演技术，提出一个新框架：仅需单张RGB漫反射贴图，即可预测全谱皮肤散射参数。该方法基于新的介质混合表示，通过三个互不相关的介质组合近似皮肤整体散射特性。我们训练了一个链式神经解码器，将RGB漫反射值映射为混合介质的光学属性（包括各向异性、散射半径、散射反照率）。最后证明，该混合介质可在基于随机游走的路径追踪器中直接使用，仅需随机选择所要穿越的介质即可实现高效渲染。
原文摘要 · Abstract (English)
In this paper we present a spectral optical inversion for skin for path tracing-based rendering of subsurface scattering. Skin is a complex multilayered medium, with appearance determined by the mixture of biophysical chromophores. However, current methods rely on medium homogeneization, with optical parameters obtained via albedo inversion from a reflectance texture and hand-tuned scattering distance and anisotropy. This results into significant art-skilled manual labor for authoring, and an inaccurate scattering profile for skin. To solve these problems, we generalize existing albedo inversion techniques, and propose a framework that predicts full-spectral skin scattering parameters from a single RGB diffuse albedo. Our method builds upon a new mixture-of-media representation, that approximates the aggregated multilayered appearance of skin by mixing the aggregated scattering of three uncorrelated media. We train a chained neural decoder that maps RGB diffuse albedo to the optical properties of the mixture of media, including anisotropy, scattering radius and scattering albedo. Then, we show this mixture can be used in a random-walk-based path tracer with minimal modifications, by simply randomly selecting the medium to traverse.
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