  Vector font synthesis is a challenging and ongoing problem in the fields of
Computer Vision and Computer Graphics. The recently-proposed DeepVecFont
achieved state-of-the-art performance by exploiting information of both the
image and sequence modalities of vector fonts. However, it has limited
capability for handling long sequence data and heavily relies on an
image-guided outline refinement post-processing. Thus, vector glyphs
synthesized by DeepVecFont still often contain some distortions and artifacts
and cannot rival human-designed results. To address the above problems, this
paper proposes an enhanced version of DeepVecFont mainly by making the
following three novel technical contributions. First, we adopt Transformers
instead of RNNs to process sequential data and design a relaxation
representation for vector outlines, markedly improving the model's capability
and stability of synthesizing long and complex outlines. Second, we propose to
sample auxiliary points in addition to control points to precisely align the
generated and target B\'ezier curves or lines. Finally, to alleviate error
accumulation in the sequential generation process, we develop a context-based
self-refinement module based on another Transformer-based decoder to remove
artifacts in the initially synthesized glyphs. Both qualitative and
quantitative results demonstrate that the proposed method effectively resolves
those intrinsic problems of the original DeepVecFont and outperforms existing
approaches in generating English and Chinese vector fonts with complicated
structures and diverse styles.

Computer Vision and Pattern Recognition;Graphics