An Improved Generative Design Approach based on Graph Grammar for Pattern Drawing

• Autorzy: Liu, Yufeng , Zhou, Yangchen , Yang, Fan , Li, Song , Wu, Jun
• Języki publikacji: EN

Abstrakty

EN

Generative design is used to efficiently generate design solutions with powerful computational methods. Generative design based on shape grammar is currently the most commonly used approach, but it is difficult for shape grammar to formally analyze the generated pattern. Graph grammar derived from one-dimensional character grammar is mainly used for generating and analyzing abstract models of visual languages. However, there is a significant gap between the generated node-edge graphs and the representation of shape appearance. To address these problems, we propose an improved generative design approach based on virtual-node based continuous Coordinate Graph Grammar (vcCGG). This approach defines a new type of grammatical rule named node transformation rules to convert nodes into shapes with node transformation applications. By combining node transformation applications and L-applications in vcCGG, we can generate a node-edge graph as the structure of the pattern through L-applications, and then draw the shape outline, next adjust the positions of these shapes, thus relating abstract structures and the physical layouts of visual languages. At the end of the paper, we provide an example application of this approach: generating an illustration from Emma Talbot using a combination of node transformation applications and L-applications.

Słowa kluczowe

EN

generative design; graph grammar; shape grammar; node transformation rules; pattern drawing

• Czasopismo: Machine Graphics and Vision
• Rocznik: 2024
• Tom: Vol. 33, No. 1
• Strony: 3--20
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Liu, Yufeng

• College of Information Engineering; Nanjing University of Finance and Economics; Nanjing; China,

autor

Zhou, Yangchen

• College of Information Engineering; Nanjing University of Finance and Economics; Nanjing; China

autor

Yang, Fan

• College of Information Engineering; Nanjing University of Finance and Economics; Nanjing; China

autor

Li, Song

• College of Information Engineering; Nanjing University of Finance and Economics; Nanjing; China

autor

Wu, Jun

• College of Information Engineering; Nanjing University of Finance and Economics; Nanjing; China

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Identyfikatory

DOI

10.22630/MGV.2024.33.1.1