Light Sampling Field and BRDF Representation for Physically-based Neural Rendering

TL;DR

This paper introduces a physically-based neural rendering method that models complex lighting and BRDF properties for realistic face skin rendering, eliminating device dependency and improving performance.

Contribution

It proposes a novel light sampling field and BRDF models for neural rendering, enabling accurate and efficient PBR of complex materials like translucent skin.

Findings

Achieves photo-realistic face skin rendering with high quality.

Demonstrates significant performance improvements over traditional methods.

Effectively models complex lighting and material properties.

Abstract

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.