arXiv:2606.16502cs.CV2026-06中稿 · ICDAR2026

主动挑选参考字形,用更少次数提升字体生成质量。

Active Reference Acquisition in Few-Shot Font Generation

论文配图:Active Reference Acquisition in Few-Shot Font Generation
图 1 · 摘自论文原文
  • 根据字形局部结构特征,动态选择最能补充视觉部件的字符
  • 在谷歌字体数据集上,用更少查询次数达到更高生成质量
  • 适合需要高效生成新字体的设计师或自动化设计工具

少样本字体生成旨在仅凭少量参考字形,保持风格一致地合成字体中剩余字形,辅助字体设计师高效完成字型。现有方法多聚焦于固定参考集下的生成质量提升,但当当前参考字形无法充分代表目标风格时,生成效果会下降。实际场景中,可向设计师获取额外参考字形。为此,我们提出一种新的框架——少样本字体生成中的主动参考获取,使模型可逐步决定下一个应获取的字符作为额外参考。进一步提出基于参考部件覆盖率的查询函数,以高效请求设计师。受字体风格由局部结构部件刻画的启发,我们用局部特征直方图表示每个字形,并选择能最大化参考集预期部件覆盖度的字符进行查询。通过优先选择当前参考未覆盖的部件,该方法逐步扩展参考集的视觉部件多样性,从而在更少查询次数下提升生成质量。在 Google Fonts 数据集上的实验表明,该方法优于随机查询和无参考依赖基线。代码已开源:https://github.com/matsuo-shinnosuke/ActiveRef-FontGen。

原文摘要 · Abstract (English)

Few-shot font generation aims to synthesize the remaining glyphs of a font given one or a few reference glyphs while preserving stylistic consistency, thereby supporting font designers in efficiently completing a typeface. Existing methods primarily focus on improving generation quality given a fixed reference set. However, when the current reference glyphs are insufficient to represent the target style, few-shot font generation may fail to produce satisfactory results. In practical scenarios, additional reference glyphs can often be obtained from the designer when necessary. Accordingly, we propose a new framework, Active Reference Acquisition in Few-Shot Font Generation, in which the model sequentially decides which character to acquire next as an additional reference. Furthermore, we propose a reference part-coverage-based acquisition function to efficiently query the designer. Motivated by the observation that font styles are well characterized by local structural parts, we represent each glyph using a histogram of local features and select query characters that maximize the expected part coverage of the reference set. By prioritizing characters that contain parts not yet covered by the current references, the proposed method progressively expands the diversity of visual parts in the reference set. As a result, generation quality is improved with fewer queries. Experiments on the Google Fonts dataset demonstrate that the proposed method achieves higher generation quality than random querying and reference-agnostic baselines. The code is available at https://github.com/matsuo-shinnosuke/ActiveRef-FontGen.

字体生成少样本学习主动学习

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