Font design is of vital importance in the digital content design and modern
printing industry. Developing algorithms capable of automatically synthesizing
vector fonts can significantly facilitate the font design process. However,
existing methods mainly concentrate on raster image generation, and only a few
approaches can directly synthesize vector fonts. This paper proposes an
end-to-end trainable method, VecFontSDF, to reconstruct and synthesize
high-quality vector fonts using signed distance functions (SDFs). Specifically,
based on the proposed SDF-based implicit shape representation, VecFontSDF
learns to model each glyph as shape primitives enclosed by several parabolic
curves, which can be precisely converted to quadratic B\'ezier curves that are
widely used in vector font products. In this manner, most image generation
methods can be easily extended to synthesize vector fonts. Qualitative and
quantitative experiments conducted on a publicly-available dataset demonstrate
that our method obtains high-quality results on several tasks, including vector
font reconstruction, interpolation, and few-shot vector font synthesis,
markedly outperforming the state of the art.

该研究提出了一种使用符号距离函数的隐式形状表示，通过机器学习将每个字形建模为由几个抛物线曲线围绕的形状基元，精确转换为二次贝塞尔曲线的算法，能够重建和合成高质量的矢量字体。

VecFontSDF: 通过有符号距离函数学习重建和合成高质量矢量字体

VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector  Fonts via Signed Distance Functions

Font design is now still considered as an exclusive privilege of professional
designers, whose creativity is not possessed by existing software systems.
Nevertheless, we also notice that most commercial font products are in fact
manually designed by following specific requirements on some attributes of
glyphs, such as italic, serif, cursive, width, angularity, etc. Inspired by
this fact, we propose a novel model, Attribute2Font, to automatically create
fonts by synthesizing visually-pleasing glyph images according to
user-specified attributes and their corresponding values. To the best of our
knowledge, our model is the first one in the literature which is capable of
generating glyph images in new font styles, instead of retrieving existing
fonts, according to given values of specified font attributes. Specifically,
Attribute2Font is trained to perform font style transfer between any two fonts
conditioned on their attribute values. After training, our model can generate
glyph images in accordance with an arbitrary set of font attribute values.
Furthermore, a novel unit named Attribute Attention Module is designed to make
those generated glyph images better embody the prominent font attributes.
Considering that the annotations of font attribute values are extremely
expensive to obtain, a semi-supervised learning scheme is also introduced to
exploit a large number of unlabeled fonts. Experimental results demonstrate
that our model achieves impressive performance on many tasks, such as creating
glyph images in new font styles, editing existing fonts, interpolation among
different fonts, etc.

提出 Attribute2Font 模型，通过合成视觉上令人愉悦的字形图像，自动创建用户指定属性的字体，并使用属性关注模块来突出突出显著的字体属性，同时进行半监督学习以利用大量未标记的字体。

属性转字体：从属性中创造你所需的字体

Attribute2Font: Creating Fonts You Want From Attributes

Dramatic advances in generative models have resulted in near photographic
quality for artificially rendered faces, animals and other objects in the
natural world. In spite of such advances, a higher level understanding of
vision and imagery does not arise from exhaustively modeling an object, but
instead identifying higher-level attributes that best summarize the aspects of
an object. In this work we attempt to model the drawing process of fonts by
building sequential generative models of vector graphics. This model has the
benefit of providing a scale-invariant representation for imagery whose latent
representation may be systematically manipulated and exploited to perform style
propagation. We demonstrate these results on a large dataset of fonts and
highlight how such a model captures the statistical dependencies and richness
of this dataset. We envision that our model can find use as a tool for graphic
designers to facilitate font design.

该研究利用生成模型建立向量图形的连续生成模型，从而刻画字体绘制过程，提供了一种可操作的样式传播方法，有望成为平面设计师进行字体设计的工具。