arXiv 2602.188742026-02-21cs.CVcs.AI

结构层面解耦的扩散模型用于少样本中文字体生成

Structure-Level Disentangled Diffusion for Few-Shot Chinese Font Generation

State Key Laboratory for Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室）；School of Artificial Intelligence, Nanjing University, China（人工智能学院）；Department of Computer Science and Technology, Nanjing University, China（计算机科学与技术系）；School of Electronic Science and Engineering, Nanjing University, China（电子科学与工程学院）

Jie Li, Suorong Yang, Jian Zhao, Furao Shen

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AI总结：

SLD-Font通过结构层面解耦和参数高效微调，实现少样本中文字体生成中更高的风格保真度和内容准确性。

摘要：

少样本中文字体生成旨在仅使用少量参考图像合成目标风格的新字符。实现准确的内容渲染和忠实的风格迁移需要有效的内容与风格解耦。然而，现有方法仅实现特征层面的解耦，允许生成器重新耦合这些特征，导致内容失真和风格保真度下降。我们提出了结构层面解耦的扩散模型（SLD-Font），该模型从两个独立通道接收内容和风格信息。SimSun风格图像用作内容模板，并与噪声潜在特征拼接作为输入。通过CLIP模型从目标风格图像中提取的风格特征通过交叉注意力进行整合。此外，我们还在像素空间中训练了一个背景噪声去除模块，以去除复杂笔画区域的背景噪声。基于解耦有效性理论验证，我们引入了一种参数高效的微调策略，仅更新与风格相关的模块。这使模型能够更好地适应新风格，同时避免过度拟合参考图像的内容。我们进一步引入灰度和OCR指标来评估生成字符的内容质量。实验结果表明，SLD-Font在保持与现有最先进方法相当的内容准确性的同时，实现了显著更高的风格保真度。

英文摘要：

Few-shot Chinese font generation aims to synthesize new characters in a target style using only a handful of reference images. Achieving accurate content rendering and faithful style transfer requires effective disentanglement between content and style. However, existing approaches achieve only feature-level disentanglement, allowing the generator to re-entangle these features, leading to content distortion and degraded style fidelity. We propose the Structure-Level Disentangled Diffusion Model (SLD-Font), which receives content and style information from two separate channels. SimSun-style images are used as content templates and concatenated with noisy latent features as the input. Style features extracted by a CLIP model from target-style images are integrated via cross-attention. Additionally, we train a Background Noise Removal module in the pixel space to remove background noise in complex stroke regions. Based on theoretical validation of disentanglement effectiveness, we introduce a parameter-efficient fine-tuning strategy that updates only the style-related modules. This allows the model to better adapt to new styles while avoiding overfitting to the reference images' content. We further introduce the Grey and OCR metrics to evaluate the content quality of generated characters. Experimental results show that SLD-Font achieves significantly higher style fidelity while maintaining comparable content accuracy to existing state-of-the-art methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。