A Hyperspectral Space of Skin Tones for Inverse Rendering of Biophysical Skin Properties

We present a method for estimating the main properties of human skin, leveraging a hyperspectral dataset of skin tones synthetically generated through a biophysical layered skin model and Monte Carlo light transport simulations. Our approach learns the mapping between the skin parameters and diffuse skin reflectance in such space through an encoder-decoder network. We assess the performance of RGB and spectral reflectance up to 1 mu m, allowing the model to retrieve visible and near-infrared. Instead of restricting the parameters to values in the ranges reported in medical literature, we allow the model to exceed such ranges to gain expressiveness to recover outliers like beard, eyebrows, rushes and other imperfections. The continuity of our albedo space allows to recover smooth textures of skin properties, enabling reflectance manipulations by meaningful edits of the skin properties. The space is robust under different illumination conditions, and presents high spectral similarity with the current largest datasets of spectral measurements of real human skin while expanding its gamut.

Automated summary

We propose a method for estimating human skin properties using a hyperspectral dataset generated through simulations. Our approach learns the mapping between skin parameters and diffuse skin reflectance using a neural network. The model allows for a wider range of parameters to recover outliers and provides smooth texture manipulations of skin properties. The albedo space is robust and expands the gamut of spectral measurements of real human skin.