  A few-shot font generation (FFG) method has to satisfy two objectives: the
generated images should preserve the underlying global structure of the target
character and present the diverse local reference style. Existing FFG methods
aim to disentangle content and style either by extracting a universal
representation style or extracting multiple component-wise style
representations. However, previous methods either fail to capture diverse local
styles or cannot be generalized to a character with unseen components, e.g.,
unseen language systems. To mitigate the issues, we propose a novel FFG method,
named Multiple Localized Experts Few-shot Font Generation Network (MX-Font).
MX-Font extracts multiple style features not explicitly conditioned on
component labels, but automatically by multiple experts to represent different
local concepts, e.g., left-side sub-glyph. Owing to the multiple experts,
MX-Font can capture diverse local concepts and show the generalizability to
unseen languages. During training, we utilize component labels as weak
supervision to guide each expert to be specialized for different local
concepts. We formulate the component assign problem to each expert as the graph
matching problem, and solve it by the Hungarian algorithm. We also employ the
independence loss and the content-style adversarial loss to impose the
content-style disentanglement. In our experiments, MX-Font outperforms previous
state-of-the-art FFG methods in the Chinese generation and cross-lingual, e.g.,
Chinese to Korean, generation. Source code is available at
https://github.com/clovaai/mxfont.
