Typeface generation through style descriptions with generative models
File(s)
Author(s)
Type
Conference Paper
Abstract
Typeface design plays a vital role in graphic and communication design. Different typefaces are suitable for different contexts and can convey different emotions and messages. Typeface design still relies on skilled designers to create unique styles for specific needs. Recently, generative adversarial networks (GANs) have been applied to typeface generation, but these methods face challenges due to the high annotation requirements of typeface generation datasets, which are difficult to obtain. Furthermore, machine-generated typefaces often fail to meet designers’ specific requirements, as dataset annotations limit the diversity of the generated typefaces. In response to these limitations in current typeface generation models, we propose an alternative approach to the task. Instead of relying on dataset-provided annotations to define the typeface style vector, we introduce a transformer-based language model to learn the mapping between a typeface style description and the corresponding style vector. We evaluated the proposed model using both existing and newly created style descriptions. Results indicate that the model can generate high-quality, patent-free typefaces based on the input style descriptions provided by designers. The code is available at: https://github.com/tqxg2018/Description2Typeface
Date Issued
2025-01-19
Date Acceptance
2024-12-01
Citation
VRCAI '24: Proceedings of the 19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, 2025, pp.1-12
ISBN
9798400713484
Publisher
Association for Computing Machinery
Start Page
1
End Page
12
Journal / Book Title
VRCAI '24: Proceedings of the 19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
Copyright Statement
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License (http://creativecommons.org/licenses/by/4.0/)
License URL
Source
19th International Conference on Virtual-Reality Continuum and its Applications in Industry
Subjects
Artificial Intelligence
Computer Science
Computer Science, Cybernetics
Computer Science, Interdisciplinary Applications
Computer vision
Generative Adversarial Networks
Science & Technology
Technology
Typeface Design
Typeface generation
Publication Status
Published
Start Date
2024-12-01
Finish Date
2024-12-02
Coverage Spatial
Nanjing, China
Date Publish Online
2024-12-01