通过迁移相似性引导的全局风格和量化的局部风格进行少样本字体生成
Few shot font generation via transferring similarity guided global style and quantization local style
AI总结：
提出一种新颖的少样本字体生成方法，通过字符相似性引导聚合全局风格特征，并利用向量量化自学习离散组件结合交叉注意力迁移局部风格，有效捕捉局部细节并控制全局特征，在多语言上表现优越。
摘要：
自动少样本字体生成（AFFG）旨在仅用少量字形参考即可生成新字体，从而减少了手动设计字体的劳动成本。然而，传统的基于风格-内容解耦的AFFG范式无法捕捉不同字体的多样局部细节。因此，提出了许多基于组件的方法来解决这个问题。基于组件的方法存在的问题是，它们通常需要特殊的预定义字形组件，例如笔画和部首，这对于不同语言的AFFG是不可行的。在本文中，我们提出了一种新颖的字体生成方法，通过聚合来自字符相似性引导的全局特征和风格化组件级表示的风格。我们通过测量内容特征中对应通道之间的距离来计算目标字符和参考样本的相似性分数，并将其作为聚合全局风格特征的权重。为了更好地捕捉局部风格，采用了一个基于交叉注意力的风格迁移模块，将参考字形的风格迁移到组件上，其中组件是通过向量量化自学习的离散潜在码，无需手动定义。通过这些设计，我们的AFFG方法能够获得一套完整的组件级风格表示，同时控制全局字形特征。实验结果反映了该方法在不同语言文字上的有效性和泛化能力，并展示了其与其他最先进方法相比的优越性。源代码可在https://github.com/awei669/VQ-Font找到。
英文摘要：
Automatic few-shot font generation (AFFG), aiming at generating new fonts with only a few glyph references, reduces the labor cost of manually designing fonts. However, the traditional AFFG paradigm of style-content disentanglement cannot capture the diverse local details of different fonts. So, many component-based approaches are proposed to tackle this problem. The issue with component-based approaches is that they usually require special pre-defined glyph components, e.g., strokes and radicals, which is infeasible for AFFG of different languages. In this paper, we present a novel font generation approach by aggregating styles from character similarity-guided global features and stylized component-level representations. We calculate the similarity scores of the target character and the referenced samples by measuring the distance along the corresponding channels from the content features, and assigning them as the weights for aggregating the global style features. To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition. With these designs, our AFFG method could obtain a complete set of component-level style representations, and also control the global glyph characteristics. The experimental results reflect the effectiveness and generalization of the proposed method on different linguistic scripts, and also show its superiority when compared with other state-of-the-art methods. The source code can be found at https://github.com/awei669/VQ-Font.
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。
