Generative models for material creation are fundamentally limited by the quality and expressivity of available training data. Simple physically based rendering (PBR) materials, which combine a diffuse term with a single-lobe specular component, are commonly used for training but are insufficient to capture many important visual effects present in real materials. We present a method that enhances such simple PBR materials to more expressive ones, by augmenting the single GGX specular lobe into a layered model that captures a broader range of non-diffuse effects. Starting from a simple material, we procedurally construct a corresponding multi-lobe non-diffuse component guided by physical priors, enabling effects such as dust, clearcoat, and layered scattering. To provide a compact representation for downstream applications, we encode this non-diffuse component as a neural material with a shared 6D latent space, where each material instance is represented by two latent textures and decoded by a pretrained universal MLP. We further regularize the latent space to support material generation. The resulting neural material dataset enables training generative models for richer material creation. To demonstrate this application, we finetune a video diffusion model to produce neural latent textures that encode our multi-lobe material, and present generative results as proof of feasibility. Our procedural data enhancement approach is an important step toward improving expressivity in material generation.

翻译：材质生成生成模型受限于训练数据的质量与表达能力。现有训练常用基于简单物理渲染（PBR）的材质，即结合漫反射项与单叶镜面反射分量，但这类模型难以捕捉真实材质中诸多重要的视觉效果。本文提出一种方法，通过将单一GGX镜面叶扩展为能够涵盖更广泛非漫射效应的分层模型，将这类简单PBR材质增强为更具表现力的材质。我们从基础材质出发，基于物理先验过程化构建对应的多叶非漫射分量，实现灰尘、透明涂层与分层散射等特效。为适配下游应用的紧凑表示需求，我们将该非漫射分量编码为具有共享六维潜在空间的神经材质，其中各材质实例用两个潜在纹理表示，并由预训练的通用多层感知机解码。我们进一步对潜在空间进行正则化以支持材质生成。由此得到的神经材质数据集能够训练生成模型，实现更丰富的材质创建。为证明该应用可行性，我们对视频扩散模型进行微调，使其生成编码多叶材质的神经潜在纹理，并给出生成结果。本文提出的过程化数据增强方法，是提升材质生成表现力的重要进展。