Abstract
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.
Abstract (translated)
少量样本中文字体生成的目标是使用少量参考图像来合成目标风格下的新字符。为了实现准确的内容呈现和忠实的风格转换，需要在内容与风格之间进行有效的解耦合。然而，现有的方法仅实现了特征级别的解耦合，这允许生成器重新结合这些特征，导致内容扭曲和风格保真度降低。我们提出了结构级解耦扩散模型（SLD-Font），该模型从两个独立的通道接收内容和风格信息。使用SimSun样式的图像作为内容模板，并将其与噪声潜在特性拼接在一起作为输入。通过跨注意力机制将CLIP模型从目标风格图像中提取的风格特征集成进来。此外，我们训练了一个背景噪声移除模块，在像素空间内删除复杂笔画区域中的背景噪声。基于对解耦有效性理论验证的基础上，我们引入了一种参数高效的微调策略，该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。进一步地，我们引入了Grey和OCR指标来评估生成字符的内容质量。实验结果显示，SLD-Font在保持现有最先进的方法相当的内容准确度的同时，实现了显著更高的风格保真度。
URL
https://arxiv.org/abs/2602.18874
