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.

翻译：小样本字体生成旨在通过给定一个或几个参考字形，合成该字体的其余字形，同时保持风格一致性，从而支持字体设计师高效完成整套字体的创建。现有方法主要关注在固定参考集条件下提升生成质量。然而，当当前参考字形不足以代表目标风格时，小样本字体生成可能无法产生令人满意的结果。在实际场景中，设计师通常可以在必要时提供额外的参考字形。为此，我们提出了一种新框架——小样本字体生成中的主动参考获取，其中模型按顺序决定下一个需要获取哪个字符作为额外参考。此外，我们提出了一种基于参考部件覆盖率的获取函数，以高效地向设计师进行查询。基于字体风格由局部结构部件良好表征的观察，我们利用局部特征直方图表示每个字形，并选择能够最大化参考集预期部件覆盖率的查询字符。通过优先选择包含当前参考集中尚未覆盖部件的字符，所提方法逐步扩展参考集中视觉部件的多样性。因此，生成质量得以提升，且查询次数更少。在Google字体数据集上的实验表明，所提方法在生成质量上优于随机查询和与参考无关的基线方法。代码已开源至https://github.com/matsuo-shinnosuke/ActiveRef-FontGen。