CONSTANT：通过补丁对比增强和风格感知量化实现高质量单次手写生成
CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization
AI总结：
CONSTANT通过补丁对比增强和风格感知量化，实现高质量单次手写生成，有效提升图像细节与风格适应性。
摘要：
单次风格化手写图像生成尽管近年来取得了令人印象深刻的结果，但仍然具有挑战性，因为仅使用单个参考图像难以捕捉人类手写的复杂和多样的特征。现有方法仍然难以生成视觉上吸引人且逼真的手写图像，并且难以适应复杂的、未见过的作家风格，难以分离不变的风格特征（例如斜率、笔宽、曲率）同时忽略无关的噪声。为了解决这个问题，我们引入了通过去噪扩散（CONSTANT）进行补丁对比增强和风格感知量化，这是一种新的单次手写生成方法。CONSTANT利用三个关键创新：1）一个风格感知量化（SAQ）模块，该模块将风格建模为离散的视觉令牌，捕捉不同的概念；2）一个对比目标，确保这些令牌在嵌入的风格空间中被良好分离且有意义；3）一个潜在补丁基于对比（LLatentPCE）目标，通过在潜在空间中对生成和真实特征的多尺度空间补丁进行对齐来提高质量和局部结构。在多个语言的基准数据集上进行了广泛的实验和分析，包括英语、中文和我们提出的ViHTGen数据集（用于越南语），证明了该方法在适应新参考风格和生成高度细节图像方面的优越性，优于最先进的方法。代码可在GitHub上获得。
英文摘要：
One-shot styled handwriting image generation, despite achieving impressive results in recent years, remains challenging due to the difficulty in capturing the intricate and diverse characteristics of human handwriting by using solely a single reference image. Existing methods still struggle to generate visually appealing and realistic handwritten images and adapt to complex, unseen writer styles, struggling to isolate invariant style features (e.g., slant, stroke width, curvature) while ignoring irrelevant noise. To tackle this problem, we introduce Patch Contrastive Enhancement and Style-Aware Quantization via Denoising Diffusion (CONSTANT), a novel one-shot handwriting generation via diffusion model. CONSTANT leverages three key innovations: 1) a Style-Aware Quantization (SAQ) module that models style as discrete visual tokens capturing distinct concepts; 2) a contrastive objective to ensure these tokens are well-separated and meaningful in the embedding style space; 3) a latent patch-based contrastive (LLatentPCE) objective help improving quality and local structures by aligning multiscale spatial patches of generated and real features in latent space. Extensive experiments and analysis on benchmark datasets from multiple languages, including English, Chinese, and our proposed ViHTGen dataset for Vietnamese, demonstrate the superiority of adapting to new reference styles and producing highly detailed images of our method over state-of-the-art approaches. Code is available at GitHub
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。
