用扩散模型实现零样本段落级手写模仿，保持风格与布局一致性。
Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models
- 基于改进的潜在扩散模型，融合风格图与文本双模态条件。
- 在未见书写风格下仍保持61% mAP与56%准确率，显著超越此前最佳方法。
- 适合手写生成、数字取证及古籍数字化研究者使用。
手写模仿通常局限于生成单个单词或行，需拼接多段合成段落或整页，导致风格一致性和版式信息丢失。为此，我们提出一种段落级手写模仿方法，可适配未见书写风格。通过修改潜在扩散模型，在编码器-解码器机制中引入专门损失函数，显式保留风格与内容。增强注意力机制以支持自适应2D位置编码，并扩展条件机制以同时处理风格图像与目标文本。该方法显著提升生成手写的逼真度。在综合评估中创下新基准：风格保持的mAP达61%，顶级准确率为56%，远超此前最优方法（37% mAP，30% top-1）。代码已公开，支持可复现性与相关研究：https://github.com/M4rt1nM4yr/paragraph_handwriting_imitation_ldm
原文摘要 · Abstract (English)
The imitation of cursive handwriting is mainly limited to generating handwritten words or lines. Multiple synthetic outputs must be stitched together to create paragraphs or whole pages, whereby consistency and layout information are lost. To close this gap, we propose a method for imitating handwriting at the paragraph level that also works for unseen writing styles. Therefore, we introduce a modified latent diffusion model that enriches the encoder-decoder mechanism with specialized loss functions that explicitly preserve the style and content. We enhance the attention mechanism of the diffusion model with adaptive 2D positional encoding and the conditioning mechanism to work with two modalities simultaneously: a style image and the target text. This significantly improves the realism of the generated handwriting. We set a new benchmark in our comprehensive evaluation, achieving 61 % mAP and 56 % top-1 accuracy in style preservation, significantly outperforming the previous best method (37 % mAP, 30 % top-1). We are making our code publicly available for reproducibility, supporting research in this area and research into potential countermeasures: https://github.com/M4rt1nM4yr/paragraph_handwriting_imitation_ldm
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。