In this work, we explore the problem of generating fantastic special-effects
for the typography. It is quite challenging due to the model diversities to
illustrate varied text effects for different characters. To address this issue,
our key idea is to exploit the analytics on the high regularity of the spatial
distribution for text effects to guide the synthesis process. Specifically, we
characterize the stylized patches by their normalized positions and the optimal
scales to depict their style elements. Our method first estimates these two
features and derives their correlation statistically. They are then converted
into soft constraints for texture transfer to accomplish adaptive multi-scale
texture synthesis and to make style element distribution uniform. It allows our
algorithm to produce artistic typography that fits for both local texture
patterns and the global spatial distribution in the example. Experimental
results demonstrate the superiority of our method for various text effects over
conventional style transfer methods. In addition, we validate the effectiveness
of our algorithm with extensive artistic typography library generation.

本文研究探讨了在字体设计中生成出色特效的问题，并提出了一种基于空间分布统计分析的自适应多尺度纹理合成算法，通过软约束实现对样例全局空间分布和局部纹理图案的逐层合成，使样例的艺术字体与本地纹理和全局形态之间的匹配更加自然，相较于传统样式转移方法，本方法在各种文本效果上表现出更高的优越性。