Toward Richer Material Generation via Procedural Data Enhancement

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.