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MatFuse: Controllable Material Generation with Diffusion Models

About

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 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 representation for each map. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore 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 materials are publicly available at https://gvecchio.com/matfuse.

Giuseppe Vecchio, Renato Sortino, Simone Palazzo, Concetto Spampinato• 2023

Related benchmarks

TaskDatasetResultRank
Material generationSVBRDF datasets (test)
CLIP-IQA0.431
4
Image-to-Material GenerationSingle real-world images
CLIP Score (Basecolor)0.833
3
Fabric Material GenerationDiverse fabric text prompts (test)
CLIP Score0.24
3
Text-to-material generationMaterialPicker (test)
CLIP Score26.1
3
Fabric Material GenerationDiverse fabric text and image prompts (test)
CLIP-I Score0.722
2
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