4 accepted papers
We introduce StableMaterials, a novel approach for generating photorealistic physically-based rendering (PBR) materials that integrate semi-supervised learning with Latent Diffusion Models (LDMs). Our method employs adversarial training to distill knowledge from existing large-scale image generation
Creating high-quality materials in computer graphics is a challenging and time-consuming task which requires great expertise. To simplify this process we introduce MatFuse a unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials. Our method…
We introduce MatSynth a dataset of 4000+ CC0 ultra-high resolution PBR materials. Materials are crucial components of virtual relightable assets defining the interaction of light at the surface of geometries. Given their importance significant research effort was dedicated to their representation cr…
In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image. We pose the problem as an image translation task and propose a novel patch-based generative adversarial network (GAN) th…