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.

翻译：基于物理的渲染是行业广泛用于展示计算机图形资产中精细写实场景的关键沉浸式渲染技术，但其计算成本高昂且依赖复杂采集设备。受近期体素神经渲染在质量与效率上的成功启发，我们致力于开发基于物理的神经着色器以消除设备依赖性并显著提升性能。然而，现有神经渲染方法中的光照与材质模型均无法准确表征基于物理渲染所需的完整光照模型及双向反射分布函数特性。为此，本文提出一种新型光照表征方法，通过学习光采样场中的局部光采样策略对直接光与间接光进行建模。同时提出BRDF模型分别表征表面/次表面散射细节，以支持半透明材质（如皮肤、玉石）等复杂物体。我们将所提表征方法集成至端到端的基于物理的神经面部皮肤着色器中，该着色器以标准面部资产（几何、反照率贴图与法线贴图）及高动态范围图像作为输入，生成照片级渲染结果。大量实验验证了该基于物理的面部皮肤着色器在质量与效率方面的优越性，表明所提光照与材质表征方法的有效性。