Toward Richer Material Generation via Procedural Data Enhancement

Abstract: 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.