# Controlling Material Appearance by Examples

arXiv: https://arxiv.org/abs/2206.14970 · HTML: https://mlsanity.com/paper/2206.14970/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2022.

## Built on by (most cited first, 10 of 10)

- [MatFuse: Controllable Material Generation with Diffusion Models](https://mlsanity.com/paper/2308.11408/) (2023)
- [MatFusion: A Generative Diffusion Model for SVBRDF Capture](https://mlsanity.com/paper/2406.06539/) (2024)
- [Materialistic: Selecting Similar Materials in Images](https://mlsanity.com/paper/2305.13291/) (2023)
- [PSDR-Room: Single Photo to Scene using Differentiable Rendering](https://mlsanity.com/paper/2307.03244/) (2023)
- [PhotoMat: A Material Generator Learned from Single Flash Photos](https://mlsanity.com/paper/2305.12296/) (2023)
- [NeuBTF: Neural fields for BTF encoding and transfer](https://mlsanity.com/paper/2307.01199/) (2023)
- [StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning](https://mlsanity.com/paper/2406.09293/) (2024)
- [Tree-Structured Shading Decomposition](https://mlsanity.com/paper/2309.07122/) (2023)
- [FROMAT: Multiview Material Appearance Transfer via Few-Shot Self-Attention Adaptation](https://mlsanity.com/paper/2512.09617/) (2025)
- [FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification](https://mlsanity.com/paper/2603.17390/) (2026)

## 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.