MatFuse: Controllable Material Generation with Diffusion Models

作者：Giuseppe Vecchio, R. Sortino, S. Palazzo, C. Spampinato · 发表于：Computer Vision and Pattern Recognition · 年份：2023 · DOI：10.1109/CVPR52733.2024.00424 · 被引用次数：55 · 研究领域：Computer Science

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 gener-ative power of diffusion models for creation and editing of 3D materials. Our method integrates multiple sources of conditioning, including color palettes, sketches, text, and pictures, enhancing creative possibilities and granting fine-grained control over material synthesis. Additionally, MatFuse enables map-level material editing capabilities through latent manipulation by means of a multi-encoder compression model which learns a disentangled latent rep-resentation for each map. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and ex-plore the potential of material editing. Finally, we assess the quality of the generated materials both quantitatively in terms of CLIP-IQA and FID scores and qualitatively by conducting a user study. Source code for training MatFuse and supplemental mate-rials are publicly available at https: //gvecchio.com/ matfuse.