Learning physically based material and lighting decompositions for face editing

Abstract

Lighting is crucial for portrait photography, yet the complex interactions between the skin and incident light are expensive to model computationally in graphics and difficult to reconstruct analytically via computer vision. Alternatively, to allow fast and controllable reflectance and lighting editing, we developed a physically based decomposition through deep learned priors from path-traced portrait images. Previous approaches that used simplified material models or low-frequency or low-dynamic-range lighting struggled to model specular reflections or relight directly without intermediate decomposition. However, we estimate the surface normal, skin albedo and roughness, and high-frequency HDRI maps, and propose an architecture to estimate both diffuse and specular reflectance components. In our experiments, we show that this approach can represent the true appearance function more effectively than simpler baseline methods, leading to better generalization and higher-quality editing.