MatFormer

Author:

Guerrero Paul1ORCID,Hašan Miloš2ORCID,Sunkavalli Kalyan2ORCID,Měch Radomír2ORCID,Boubekeur Tamy3ORCID,Mitra Niloy J.4ORCID

Affiliation:

1. Adobe Research, UK

2. Adobe Research

3. Adobe Research, France

4. Adobe Research, UK and University College London, UK

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ControlMat: A Controlled Generative Approach to Material Capture;ACM Transactions on Graphics;2024-09-11

2. Mesh Neural Cellular Automata;ACM Transactions on Graphics;2024-07-19

3. Learning layout generation for virtual worlds;Computational Visual Media;2024-05-02

4. Learned Inference of Annual Ring Pattern of Solid Wood;Computer Graphics Forum;2024-04-22

5. DiffMat: Latent diffusion models for image-guided material generation;Visual Informatics;2024-03

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