  Chinese vector font generation is challenging due to the complex structure
and huge amount of Chinese characters. Recent advances remain limited to
generating a small set of characters with simple structure. In this work, we
first observe that most Chinese characters can be disassembled into
frequently-reused components. Therefore, we introduce the first efficient and
scalable Chinese vector font generation approach via component composition,
allowing generating numerous vector characters from a small set of components.
To achieve this, we collect a large-scale dataset that contains over
\textit{90K} Chinese characters with their components and layout information.
Upon the dataset, we propose a simple yet effective framework based on spatial
transformer networks (STN) and multiple losses tailored to font characteristics
to learn the affine transformation of the components, which can be directly
applied to the B\'ezier curves, resulting in Chinese characters in vector
format. Our qualitative and quantitative experiments have demonstrated that our
method significantly surpasses the state-of-the-art vector font generation
methods in generating large-scale complex Chinese characters in both font
generation and zero-shot font extension.
