# MatFormer: A Generative Model for Procedural Materials

arXiv: https://arxiv.org/abs/2207.01044 · HTML: https://mlsanity.com/paper/2207.01044/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2022.

## Built on by (most cited first, 12 of 12)

- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [ControlMat: A Controlled Generative Approach to Material Capture](https://mlsanity.com/paper/2309.01700/) (2023)
- [MatFusion: A Generative Diffusion Model for SVBRDF Capture](https://mlsanity.com/paper/2406.06539/) (2024)
- [PSDR-Room: Single Photo to Scene using Differentiable Rendering](https://mlsanity.com/paper/2307.03244/) (2023)
- [FaccAID: A Transformer Model for Neuro-Symbolic Facade Reconstruction](https://mlsanity.com/paper/2406.01829/) (2024)
- [Chord: Chain of Rendering Decomposition for PBR Material Estimation from Generated Texture Images](https://mlsanity.com/paper/2509.09952/) (2025)
- [Tree-Structured Shading Decomposition](https://mlsanity.com/paper/2309.07122/) (2023)
- [MaRI: Material Retrieval Integration across Domains](https://mlsanity.com/paper/2503.08111/) (2025)
- [RealMat: Realistic Materials with Diffusion and Reinforcement Learning](https://mlsanity.com/paper/2509.01134/) (2025)
- [Coevolution of Camouflage](https://mlsanity.com/paper/2304.11793/) (2023)
- [HiMat: DiT-based Ultra-High Resolution SVBRDF Generation](https://mlsanity.com/paper/2508.07011/) (2025)
- [ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2605.15081/) (2026)

## Abstract (arXiv)

Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural materials requires significant expertise and publicly accessible libraries contain only a few thousand such graphs. We present MatFormer, a generative model that can produce a diverse set of high-quality procedural materials with complex spatial patterns and appearance. While procedural materials can be modeled as directed (operation) graphs, they contain arbitrary numbers of heterogeneous nodes with unstructured, often long-range node connections, and functional constraints on node parameters and connections. MatFormer addresses these challenges with a multi-stage transformer-based model that sequentially generates nodes, node parameters, and edges, while ensuring the semantic validity of the graph. In addition to generation, MatFormer can be used for the auto-completion and exploration of partial material graphs. We qualitatively and quantitatively demonstrate that our method outperforms alternative approaches, in both generated graph and material quality.