Text effects transfer via distribution-aware texture synthesis

摘要

In this paper, we explore the problem of fantastic special-effects synthesis for the typography. The main challenge of this problem lies in the model diversities to illustrate varied text effects for different characters. To address this issue, we exploit the key analytics on the high regularity of the texture spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions relative to the text skeleton 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 well consists with both local texture patterns and the global spatial distribution in the source example. Furthermore, stroke similarities are considered to control the varieties of text effects among multiple characters in a word. Experimental results demonstrate the superiority of our distribution-aware 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 and apply our method to a general application of special effects transfer for stroke-based graphics.