MatFuse: Controllable Material Generation with Diffusion Models

Giuseppe Vecchio, R. Sortino, S. Palazzo, C. Spampinato
2/14/2026

No official code found. Best implementation: giuvecchio/matfuse (62 stars, MIT, last commit 2026-02-19).

Abstract

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.

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Code Implementations(1)

giuvecchio/matfuseCommunity100%
MIT

MatFuse: Controllable Material Generation with Diffusion Models (CVPR2024)

627PythonFeb 20, 20248 months agoMIT
cvpr2024diffusion-modelsgenerative-aipbr-materialsrendering+1 more

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Cite this paper

@article{vecchio2026matfuse,
  title  = {MatFuse: Controllable Material Generation with Diffusion Models},
  author = {Giuseppe Vecchio and R. Sortino and S. Palazzo and C. Spampinato},
  year   = {2026},
  eprint = {2308.11408},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/pdf/2308.11408},
  journal = {CVPR 2023 2023}
}

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