Spectral Subsurface Scattering from RGB via Biophysical Skin Inversion
This work addresses the problem of labor-intensive and inaccurate skin rendering for computer graphics practitioners, offering an automated solution that improves scattering accuracy.
The paper introduces a method to predict full-spectral skin scattering parameters from a single RGB diffuse albedo, enabling more accurate subsurface scattering rendering without manual tuning. The approach uses a chained neural decoder and a mixture-of-media representation to achieve realistic skin appearance in path tracing.
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.