  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.
