Conditional Font Generation With Content Pre‐Train and Style Filter
Automatic font generation aims to streamline the design process by creating new fonts with minimal style references. This technology significantly reduces the manual labour and costs associated with traditional font design. Image‐to‐image translation has been the dominant approach, transforming font...
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| Published in: | Computer graphics forum Vol. 44; no. 1 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Blackwell Publishing Ltd
01.02.2025
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| Subjects: | |
| ISSN: | 0167-7055, 1467-8659 |
| Online Access: | Get full text |
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| Summary: | Automatic font generation aims to streamline the design process by creating new fonts with minimal style references. This technology significantly reduces the manual labour and costs associated with traditional font design. Image‐to‐image translation has been the dominant approach, transforming font images from a source style to a target style using a few reference images. However, this framework struggles to fully decouple content from style, particularly when dealing with significant style shifts. Despite these limitations, image‐to‐image translation remains prevalent due to two main challenges faced by conditional generative models: (1) inability to handle unseen characters and (2) difficulty in providing precise content representations equivalent to the source font. Our approach tackles these issues by leveraging recent advancements in Chinese character representation research to pre‐train a robust content representation model. This model not only handles unseen characters but also generalizes to non‐existent ones, a capability absent in traditional image‐to‐image translation. We further propose a Transformer‐based Style Filter that not only accurately captures stylistic features from reference images but also handles any combination of them, fostering greater convenience for practical automated font generation applications. Additionally, we incorporate content loss with commonly used pixel‐ and perceptual‐level losses to refine the generated results from a comprehensive perspective. Extensive experiments validate the effectiveness of our method, particularly its ability to handle unseen characters, demonstrating significant performance gains over existing state‐of‐the‐art methods.
We propose a conditional generation method that incorporates pre‐trained content representations for high‐fidelity Chinese character font generation, and our style filter achieves efficient extraction of stylistic features from arbitrary combinations of references. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0167-7055 1467-8659 |
| DOI: | 10.1111/cgf.15270 |