  Physically-based rendering (PBR) is key for immersive rendering effects used
widely in the industry to showcase detailed realistic scenes from computer
graphics assets. A well-known caveat is that producing the same is
computationally heavy and relies on complex capture devices. Inspired by the
success in quality and efficiency of recent volumetric neural rendering, we
want to develop a physically-based neural shader to eliminate device dependency
and significantly boost performance. However, no existing lighting and material
models in the current neural rendering approaches can accurately represent the
comprehensive lighting models and BRDFs properties required by the PBR process.
Thus, this paper proposes a novel lighting representation that models direct
and indirect light locally through a light sampling strategy in a learned light
sampling field. We also propose BRDF models to separately represent
surface/subsurface scattering details to enable complex objects such as
translucent material (i.e., skin, jade). We then implement our proposed
representations with an end-to-end physically-based neural face skin shader,
which takes a standard face asset (i.e., geometry, albedo map, and normal map)
and an HDRI for illumination as inputs and generates a photo-realistic
rendering as output. Extensive experiments showcase the quality and efficiency
of our PBR face skin shader, indicating the effectiveness of our proposed
lighting and material representations.
