  Realistic face rendering from multi-view images is beneficial to various
computer vision and graphics applications. Due to the complex spatially-varying
reflectance properties and geometry characteristics of faces, however, it
remains challenging to recover 3D facial representations both faithfully and
efficiently in the current studies. This paper presents a novel 3D face
rendering model, namely NeuFace, to learn accurate and physically-meaningful
underlying 3D representations by neural rendering techniques. It naturally
incorporates the neural BRDFs into physically based rendering, capturing
sophisticated facial geometry and appearance clues in a collaborative manner.
Specifically, we introduce an approximated BRDF integration and a simple yet
new low-rank prior, which effectively lower the ambiguities and boost the
performance of the facial BRDFs. Extensive experiments demonstrate the
superiority of NeuFace in human face rendering, along with a decent
generalization ability to common objects.
