Robust Geometry and Reflectance Disentanglement for 3D Face
  Reconstruction from Sparse-view Images
Daisheng Jin Jiangbei Hu Baixin Xu Yuxin Dai Chen Qian Ying He
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.