用对比学习分离字形风格与结构，少样本生成更精准
DRG-Font: Dynamic Reference-Guided Few-shot Font Generation via Contrastive Style-Content Disentanglement
- 通过多尺度模块分解字形的风格与内容特征
- 动态选择最佳参考字形，提升风格一致性表现
- 适合需要高质量少样本字体生成的研究者使用
少样本字体生成旨在从少量参考字形中生成风格一致的字符。然而，从少量样本中捕捉复杂字体风格仍具挑战性，现有方法常难以保留生成字形的局部特征。本文提出DRG-Font，一种基于对比学习的字体生成策略，通过分解风格与内容嵌入空间来学习复杂字形属性。为实现最优风格监督，设计参考选择（RS）模块，动态从候选池中选取最佳风格参考。网络通过多尺度风格头块（MSHB）与多尺度内容头块（MCHB）将字形属性分解为风格与形状先验。在风格适配阶段，多融合上采样块（MFUB）结合参考风格先验与目标内容先验生成目标字形。所提方法在多个视觉与分析基准上显著优于现有最先进方法。
原文摘要 · Abstract (English)
Few-shot Font Generation aims to generate stylistically consistent glyphs from a few reference glyphs. However, capturing complex font styles from a few exemplars remains challenging, and the existing methods often struggle to retain discernible local characteristics in generated samples. This paper introduces DRG-Font, a contrastive font generation strategy that learns complex glyph attributes by decomposing style and content embedding spaces. For optimal style supervision, the proposed architecture incorporates a Reference Selection (RS) Module to dynamically select the best style reference from an available pool of candidates. The network learns to decompose glyph attributes into style and shape priors through a Multi-scale Style Head Block (MSHB) and a Multi-scale Content Head Block (MCHB). For style adaptation, a Multi-Fusion Upsampling Block (MFUB) produces the target glyph by combining the reference style prior and target content prior. The proposed method demonstrates significant improvements over state-of-the-art approaches across multiple visual and analytical benchmarks.
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