Recovering the geometry of a human head from a single image, while
factorizing the materials and illumination is a severely ill-posed problem that
requires prior information to be solved. Methods based on 3D Morphable Models
(3DMM), and their combination with differentiable renderers, have shown
promising results. However, the expressiveness of 3DMMs is limited, and they
typically yield over-smoothed and identity-agnostic 3D shapes limited to the
face region. Highly accurate full head reconstructions have recently been
obtained with neural fields that parameterize the geometry using multilayer
perceptrons. The versatility of these representations has also proved effective
for disentangling geometry, materials and lighting. However, these methods
require several tens of input images. In this paper, we introduce SIRA, a
method which, from a single image, reconstructs human head avatars with high
fidelity geometry and factorized lights and surface materials. Our key
ingredients are two data-driven statistical models based on neural fields that
resolve the ambiguities of single-view 3D surface reconstruction and appearance
factorization. Experiments show that SIRA obtains state of the art results in
3D head reconstruction while at the same time it successfully disentangles the
global illumination, and the diffuse and specular albedos. Furthermore, our
reconstructions are amenable to physically-based appearance editing and head
model relighting.

使用基于神经场的两个数据驱动统计模型，从单张图像中重建具有高保真度几何结构和因式化光线和表面材质的人头像，成功分离了全局照明和漫反射和镜面反射反射率，并使头部模型的外观编辑和照明调整成为可能。