近日，实验室一篇论文被国际顶级期刊ACM Transactions on Graphics (TOG)（SIGGRAPH 2020）接收，信息如下：
论文名称：Attribute2Font: Creating Fonts You Want From Attributes
作者列表： Y. Wang, Y. Gao, Z. Lian*
摘要：
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, ourmodel can generate glyph images in
accordancewithanarbitrarysetoffontattributevalues.Furthermore,anovel
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.