In this paper we present BioFaceNet, a deep CNN that learns to decompose a
single face image into biophysical parameters maps, diffuse and specular
shading maps as well as estimating the spectral power distribution of the scene
illuminant and the spectral sensitivity of the camera. The network comprises a
fully convolutional encoder for estimating the spatial maps with a fully
connected branch for estimating the vector quantities. The network is trained
using a self-supervised appearance loss computed via a model-based decoder. The
task is highly underconstrained so we impose a number of model-based priors.
Skin spectral reflectance is restricted to a biophysical model, we impose a
statistical prior on camera spectral sensitivities, a physical constraint on
illumination spectra, a sparsity prior on specular reflections and direct
supervision on diffuse shading using a rough shape proxy. We show convincing
qualitative results on in-the-wild data and introduce a benchmark for
quantitative evaluation on this new task.

本文介绍了一种名为 BioFaceNet 的深度 CNN，它能学习将单一的人脸图像分解成生物物理参数地图、漫反射和镜面遮蔽地图，并估计场景照明光谱功率分布和相机光谱灵敏度。