重新审视字体生成中的字形空间信息
Rethinking Glyph Spatial Information in Font Generation
把字形中的「形状」与「位置」显式解耦，并在像素空间分两阶段（低分辨率风格迁移 + 高分辨率细节增强）建模，这一思路可迁移到任何需要保留结构空间先验的少样本生成任务，如手写体合成、场景文字编辑与布局可控生成。
原文依据 we propose GlyphSpatialNet, a two-stage framework to explicitly model glyph spatial information in pixel space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论少样本字体生成（FFG）旨在从有限数量的参考样本中生成完整字体，具有重要的实用价值。然而，现有方法忽视了字形空间信息，由此导致两个关键局限。在流程层面，失真的渲染会引入空间偏差，损害矢量化与数据集质量，而缺乏统一标准又加剧了这一问题，使统一基准难以建立。在模型层面，形状与位置的隐式耦合阻碍了细粒度优化与泛化。我们在中文字体生成的背景下应对这些挑战，因为中文字形的复杂性对模型能力提出了更高要求。为此，我们首先提出一种空间保持渲染（SPR）方案，消除空间偏差并实现精确矢量化；同时，我们发布了一个采用 OFL 许可的中文字体数据集，以建立统一基准。在技术层面，我们提出 GlyphSpatialNet，这是一个两阶段框架，在像素空间中显式建模字形空间信息。第一阶段，我们设计了形状-位置解耦（SPD）架构与梯度广播模块（GBM），在低分辨率下实现字体风格迁移。第二阶段，我们设计了风格细节增强（SDE），为高分辨率输出精修风格细节。大量实验验证了所提方法的有效性。代码与数据集见 https://github.com/sp777g/GlyphSpatialNet。
英文原文摘要 1497 字符
Few-shot Font Generation (FFG) aims to create a complete font from a limited number of references, offering significant practical value. However, existing methods neglect glyph spatial information, which leads to two critical limitations. At the pipeline level, distorted rendering introduces spatial bias, impairing vectorization and dataset quality, and this problem is compounded by the lack of unified standards, which undermines a unified benchmark. At the model level, the implicit coupling of shape and position hinders fine-grained optimization and generalization. We address these challenges in the context of Chinese font generation, where glyph complexity demands superior model capability. Consequently, we first propose a Spatial-Preserving Rendering (SPR) scheme, which eliminates spatial bias and enables accurate vectorization. Alongside, we release an OFL-licensed Chinese font dataset to establish a unified benchmark. Then, technically, we propose GlyphSpatialNet, a two-stage framework to explicitly model glyph spatial information in pixel space. In first stage, we design a Shape-Position Decoupling (SPD) architecture and a Gradient Broadcasting Module (GBM) to achieve font style transfer in low resolution. In second stage, we design Style Detail Enhancement (SDE), which refines the style details for high resolution outputs. Extensive experiments demonstrate the effectiveness of our approach. Code and dataset are available at https://github.com/sp777g/GlyphSpatialNet.