Conditional Font Generation With Content Pre‐Train and Style Filter

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dc.contributor.authorHong, Yangen_US
dc.contributor.authorLi, Yinfeien_US
dc.contributor.authorQiao, Xiaojunen_US
dc.contributor.authorZhang, Junsongen_US
dc.date.accessioned2025-03-07T16:48:45Z
dc.date.available2025-03-07T16:48:45Z
dc.date.issued2024
dc.description.abstractAutomatic 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.en_US
dc.description.number1
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.15270
dc.identifier.issn1467-8659
dc.identifier.pages12
dc.identifier.urihttps://doi.org/10.1111/cgf.15270
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15270
dc.publisherEurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectimage and video processing
dc.subjectimage processing
dc.subject• Computing methodologies → Shape modelling; Shape representations; • Applied computing → Computer‐aided design
dc.titleConditional Font Generation With Content Pre‐Train and Style Filteren_US
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