  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.
