  Few-shot font generation (FFG), which aims to generate a new font with a few
examples, is gaining increasing attention due to the significant reduction in
labor cost. A typical FFG pipeline considers characters in a standard font
library as content glyphs and transfers them to a new target font by extracting
style information from the reference glyphs. Most existing solutions explicitly
disentangle content and style of reference glyphs globally or component-wisely.
However, the style of glyphs mainly lies in the local details, i.e. the styles
of radicals, components, and strokes together depict the style of a glyph.
Therefore, even a single character can contain different styles distributed
over spatial locations. In this paper, we propose a new font generation
approach by learning 1) the fine-grained local styles from references, and 2)
the spatial correspondence between the content and reference glyphs. Therefore,
each spatial location in the content glyph can be assigned with the right
fine-grained style. To this end, we adopt cross-attention over the
representation of the content glyphs as the queries and the representations of
the reference glyphs as the keys and values. Instead of explicitly
disentangling global or component-wise modeling, the cross-attention mechanism
can attend to the right local styles in the reference glyphs and aggregate the
reference styles into a fine-grained style representation for the given content
glyphs. The experiments show that the proposed method outperforms the
state-of-the-art methods in FFG. In particular, the user studies also
demonstrate the style consistency of our approach significantly outperforms
previous methods.
