This paper presents a novel two-stage approach for reconstructing human faces
from sparse-view images, a task made challenging by the unique geometry and
complex skin reflectance of each individual. Our method focuses on decomposing
key facial attributes, including geometry, diffuse reflectance, and specular
reflectance, from ambient light. Initially, we create a general facial template
from a diverse collection of individual faces, capturing essential geometric
and reflectance characteristics. Guided by this template, we refine each
specific face model in the second stage, which further considers the
interaction between geometry and reflectance, as well as the subsurface
scattering effects on facial skin. Our method enables the reconstruction of
high-quality facial representations from as few as three images, offering
improved geometric accuracy and reflectance detail. Through comprehensive
evaluations and comparisons, our method demonstrates superiority over existing
techniques. Our method effectively disentangles geometry and reflectance
components, leading to enhanced quality in synthesizing new views and opening
up possibilities for applications such as relighting and reflectance editing.
We will make the code publicly available.

本文提出了一种创新的两阶段方法，用于从稀疏视图图像重建人脸，该任务由于每个个体的独特几何和复杂皮肤反射而具有挑战性。我们的方法专注于从环境光中分解关键的面部属性，包括几何、漫反射和镜面反射。开始时，我们从多样化的个体脸部图像集创建一个通用的面部模板，捕捉关键的几何和反射特征。在第二阶段，我们在该模板的指导下对每个具体的人脸模型进行细化，进一步考虑几何和反射之间的相互作用，以及对面部皮肤的次表面散射效应。我们的方法能够从仅有的三幅图像中重建出高质量的面部表示，提供了改进的几何准确性和反射细节。通过全面的评估和比较，我们的方法表现出优越性，有效解离几何和反射成分，提高了合成新视图的质量，为再照明和反射编辑等应用开辟了可能。我们将代码公开可用。