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.
翻译:程序材料图是一个紧凑、参数和分辨率独立的图解,是编写材料的流行选择。然而,设计程序材料需要大量的专门知识,公众可以访问的图书馆只包含几千个这样的图解。我们提出MatFormer,这是一个可产生一套具有复杂的空间模式和外观的高质量程序材料的基因化模型。程序材料可以按指示(操作)图建模,但含有不结构的、往往是长距离节点连接和节点参数和连接功能限制的任意数字。MatFormer用一个基于多阶段变压器的模型来应对这些挑战,该模型按顺序生成节点、节点参数和边缘,同时确保图的语义有效性。除了生成外,MatFormer还可以用于自动完成和探索部分材料图解。我们从质量和数量上证明,我们的方法在生成的图表和材料质量上都超越了替代方法。