Towards Diverse and Consistent Typography Generation

TL;DR

This paper presents an autoregressive model for generating diverse and consistent typography styles across multiple text elements, improving design variability while maintaining visual harmony.

Contribution

It introduces a novel autoregressive approach combined with a sampling method to produce diverse yet coherent typographic designs.

Findings

Successfully generates diverse typography styles.

Maintains consistent typographic structure across text elements.

Empirical results validate the effectiveness of the approach.

Abstract

In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document. We formulate typography generation as a fine-grained attribute generation for multiple text elements and build an autoregressive model to generate diverse typography that matches the input design context. We further propose a simple yet effective sampling approach that respects the consistency and distinction principle of typography so that generated examples share consistent typographic styling across text elements. Our empirical study shows that our model successfully generates diverse typographic designs while preserving a consistent typographic structure.