将汉字手写文本拆解为布局与字形，实现风格化整行生成。
Decoupling Layout from Glyph in Online Chinese Handwriting Generation
- 分层生成：先定字位布局，再按位置生成对应风格字形。
- 在CASIA-OLHWDB上生成样本结构正确，视觉逼真度高。
- 适合需要批量生成仿手写文本的场景，如古籍数字化。
文字在人类文明传承中至关重要，让机器生成不同风格的在线手写文本是一项重要挑战。然而，以往研究多集中于单个汉字字体生成，对完整文本行生成关注不足。本文提出将文本行分解为布局与字形两部分，设计了基于扩散模型的风格化字形合成器与文本行布局生成器协同工作：布局生成器根据内容和风格参考，自回归地预测每个字的位置；字形合成器则结合字符嵌入字典、多尺度书法风格编码器及1D U-Net扩散去噪器，在指定位置生成模仿参考风格的字形。在CASIA-OLHWDB数据集上的定性与定量实验表明，该方法能生成结构正确且难以区分的仿写样本。
原文摘要 · Abstract (English)
Text plays a crucial role in the transmission of human civilization, and teaching machines to generate online handwritten text in various styles presents an interesting and significant challenge. However, most prior work has concentrated on generating individual Chinese fonts, leaving {complete text line generation largely unexplored}. In this paper, we identify that text lines can naturally be divided into two components: layout and glyphs. Based on this division, we designed a text line layout generator coupled with a diffusion-based stylized font synthesizer to address this challenge hierarchically. More concretely, the layout generator performs in-context-like learning based on the text content and the provided style references to generate positions for each glyph autoregressively. Meanwhile, the font synthesizer which consists of a character embedding dictionary, a multi-scale calligraphy style encoder, and a 1D U-Net based diffusion denoiser will generate each font on its position while imitating the calligraphy style extracted from the given style references. Qualitative and quantitative experiments on the CASIA-OLHWDB demonstrate that our method is capable of generating structurally correct and indistinguishable imitation samples.
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