Estimation of Spectral Biophysical Skin Properties from Captured RGB Albedo

TL;DR

This paper introduces a novel method to estimate and manipulate the spectral biophysical properties of human skin from simple RGB images by leveraging Monte Carlo simulations and a biophysical skin model.

Contribution

It presents an inverse mapping technique that accurately reconstructs skin properties from RGB albedo, enabling realistic editing of skin characteristics.

Findings

Accurately reconstructs skin spectral properties from RGB images.

Handles challenging skin areas like lips and imperfections.

Enables robust editing of skin biophysical parameters.

Abstract

We present a new method to reconstruct and manipulate the spectral properties of human skin from simple RGB albedo captures. To this end, we leverage Monte Carlo light simulation over an accurate biophysical human skin layering model parameterized by its most important components, thereby covering a plausible range of colors. The practical complexity of the model allows us to learn the inverse mapping from any albedo to its most probable associated skin properties. Our technique can faithfully reproduce any skin type, being expressive enough to automatically handle more challenging areas like the lips or imperfections in the face. Thanks to the smoothness of the skin parameters maps recovered, the albedo can be robustly edited through meaningful biophysical properties.