Controlling Material Appearance by Examples
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Abstract (arXiv)
Despite the ubiquitousness of materials maps in modern rendering pipelines, their editing and control remains a challenge. In this paper, we present an example-based material control method to augment input material maps based on user-provided material photos. We train a tileable version of MaterialGAN and leverage its material prior to guide the appearance transfer, optimizing its latent space using differentiable rendering. Our method transfers the micro and meso-structure textures of user provided target(s) photographs, while preserving the structure of the input and quality of the input material. We show our methods can control existing material maps, increasing realism or generating new, visually appealing materials.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jun 2022.
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Citing papers per year: 2023: 6; 2024: 2; 2025: 1; 2026: 1.
10 papers.
- 2023MatFuse: Controllable Material Generation with Diffusion Modelscited by 39
- 2024MatFusion: A Generative Diffusion Model for SVBRDF Capturecited by 31
- 2023Materialistic: Selecting Similar Materials in Imagescited by 26
- 2023PSDR-Room: Single Photo to Scene using Differentiable Renderingcited by 23
- 2023PhotoMat: A Material Generator Learned from Single Flash Photoscited by 18
- 2023NeuBTF: Neural fields for BTF encoding and transfercited by 5
- 2024StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learningcited by 4
- 2023Tree-Structured Shading Decompositioncited by 2
- 2025FROMAT: Multiview Material Appearance Transfer via Few-Shot Self-Attention Adaptationcited by 0
- 2026FMMC: Harnessing the Power of Foundation Models for Accurate Material Classificationcited by 0
- 2026FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification18 Mar 2026
- 2025FROMAT: Multiview Material Appearance Transfer via Few-Shot Self-Attention Adaptation10 Dec 2025
- 2024StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning13 Jun 2024
- 2024MatFusion: A Generative Diffusion Model for SVBRDF Capture24 Apr 2024
- 2023Tree-Structured Shading Decomposition13 Sep 2023
- 2023MatFuse: Controllable Material Generation with Diffusion Models22 Aug 2023
- 2023PSDR-Room: Single Photo to Scene using Differentiable Rendering6 Jul 2023
- 2023NeuBTF: Neural fields for BTF encoding and transfer3 Jul 2023
- 2023Materialistic: Selecting Similar Materials in Images22 May 2023
- 2023PhotoMat: A Material Generator Learned from Single Flash Photos20 May 2023