Automatic few-shot font generation aims to solve a well-defined, real-world
problem because manual font designs are expensive and sensitive to the
expertise of designers. Existing methods learn to disentangle style and content
elements by developing a universal style representation for each font style.
However, this approach limits the model in representing diverse local styles,
because it is unsuitable for complicated letter systems, for example, Chinese,
whose characters consist of a varying number of components (often called
"radical") -- with a highly complex structure. In this paper, we propose a
novel font generation method that learns localized styles, namely
component-wise style representations, instead of universal styles. The proposed
style representations enable the synthesis of complex local details in text
designs. However, learning component-wise styles solely from a few reference
glyphs is infeasible when a target script has a large number of components, for
example, over 200 for Chinese. To reduce the number of required reference
glyphs, we represent component-wise styles by a product of component and style
factors, inspired by low-rank matrix factorization. Owing to the combination of
strong representation and a compact factorization strategy, our method shows
remarkably better few-shot font generation results (with only eight reference
glyphs) than other state-of-the-art methods. Moreover, strong locality
supervision, for example, location of each component, skeleton, or strokes, was
not utilized. The source code is available at this https URL
and this https URL

本文提出了一种新的字体生成方法，该方法通过学习局部风格表达，即以组件为基础的字体风格表达，而不是普适的字体风格表达。该方法在仅有 8 个参考字符的情况下，生成效果优于其他现有方法。同时，该方法未利用强的局部学习监管。