Awesome Typography: Statistics-Based Text Effects Transfer

TL;DR

This paper introduces a statistics-guided method for transferring complex text effects to typography, enabling the creation of artistic and diverse styles by modeling spatial distributions and scale features.

Contribution

It presents a novel approach that leverages spatial regularity and statistical correlation of style elements for adaptive multi-scale texture transfer in typography.

Findings

Outperforms conventional style transfer methods in producing artistic text effects.

Effectively models local texture patterns and global spatial distributions.

Validated through extensive typography library generation.

Abstract

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.