Active Reference Acquisition in Few-Shot Font Generation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 15 Jun 2026.
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References · 13
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2017Deep Bayesian Active Learning with Image Datacited by 819
- 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
- 2017Multi-Content GAN for Few-Shot Font Style Transfercited by 100
- 2018EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAEcited by 87
- 2020Active Feature Acquisition with Generative Surrogate Modelscited by 54
- 2023FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learningcited by 48
- 2020Few-shot Font Generation with Localized Style Representations and Factorizationcited by 47
- 2021Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Expertscited by 46
- 2020Few-shot Compositional Font Generation with Dual Memorycited by 44
- 2022Diff-Font: Diffusion Model for Robust One-Shot Font Generationcited by 40
- 2022Few-Shot Font Generation by Learning Fine-Grained Local Stylescited by 34
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- 2018Active Feature Acquisition with Supervised Matrix Completioncited by 12
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