TypeDance: An AI-Assisted Workflow for Semantic Typographic Logos

Table of Links

1. Introduction

2. Related Work

   2.1 Semantic Typographic Logo Design

   2.2 Generative Model for Computational Design

   2.3 Graphic Design Authoring Tool

3. Formative Study

   3.1 General Workflow and Challenges

   3.2 Concerns in Generative Model Involvement

   3.3 Design Space of Semantic Typography Work

4. Design Consideration

5. Typedance and 5.1 Ideation

   5.2 Selection

   5.3 Generation

   5.4 Evaluation

   5.5 Iteration

6. Interface Walkthrough and 6.1 Pre-generation stage

   6.2 Generation stage

   6.3 Post-generation stage

7. Evaluation and 7.1 Baseline Comparison

   7.2 User Study

   7.3 Results Analysis

   7.4 Limitation

8. Discussion

   8.1 Personalized Design: Intent-aware Collaboration with AI

   8.2 Incorporating Design Knowledge into Creativity Support Tools

   8.3 Mix-User Oriented Design Workflow

9. Conclusion and References

Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility. Conventional methods using spatial composition and shape substitution are hindered by the conflicting requirement for achieving seamless spatial fusion between geometrically dissimilar typefaces and semantics. While recent advances made AI generation of semantic typography possible, the end-to-end approaches exclude designer involvement and disregard personalized design. This paper presents TypeDance, an AI-assisted tool incorporating design rationales with the generative model for personalized semantic typographic logo design. It leverages combinable design priors extracted from uploaded image exemplars and supports type-imagery mapping at various structural granularity, achieving diverse aesthetic designs with flexible control. Additionally, we instantiate a comprehensive design workflow in TypeDance, including ideation, selection, generation, evaluation, and iteration. A two-task user evaluation, including imitation and creation, confirmed the usability of TypeDance in design across different usage scenarios.

1 INTRODUCTION

Semantic typography is the art of blending typeface and imagery, where the typeface is conceptualized as a visual illustration of semantic representation with high clarity and legibility [21, 23, 47]. One notable application is the semantic typographic logo, which symbolizes a unique identity in a concise yet informative manner. Due to its expressiveness and memorability [7], semantic typographic logo has been widely used as visual signatures for individuals [28], brand logos with commercial values [15, 20], and symbols for significant events and city promotions [3, 43].

However, crafting a semantic typographic logo presents a formidable challenge, requiring seamless blending of typeface and imagery while preserving readability. Experienced designers often rely on professional software like Adobe Illustrator to manually adjust the outline of the typeface to incorporate specific imagery, which is a time-consuming and error-prone process. They often experiment with different strokes or letters of typeface and various imageries to find a visually appealing and memorable representation, intensifying the lengthy process. This requires creative thinking, practical skills, and the ability to persist through continuous trial and error. In addition, the unique identity of a logo necessitates a high level of customization and personalization in the design process.

There are two main challenges in extensive relevant research: blending technique and intent-aware authoring. Existing authoring tools leverage various blending techniques to create other types of designs, e.g., graphical icons. As shown in Fig. 3, One typical technique aims to spatially composite existing materials [13, 64]. Another technique uses shape substitution to achieve a more spatial merge, but it heavily depends on the shape similarity between the objects being blended [8, 9]. Although some computational design techniques support incorporating imagery into typefaces, they are constrained by the ability to change specific parts of typefaces [4, 23, 48]. Advancements in text-to-image generative models [42, 63] have made it possible to generate semantic typography automatically, but it poses another challenge about intent-aware authoring. Given the myriad details such as specific visual presentation (e.g., semantics, color, and shape) in a logo design, text prompts may not be able to represent these intents.

This research aims to gain insights into the design space and workflow involved in creating semantic typographic logos, and then instantiate these design principles to create an AI-assisted tool that facilitates personalized generation. Through analysis of a curated corpus, a systematic design space was identified, focusing on typeface granularity (i.e., stroke-, letter-, and multi-letter-level) and type-imagery mapping (i.e., one-to-one, one-to-many, and many-to-one mappings). Additionally, interviews were conducted with three experts to gather insights into the challenges and concerns regarding AI collaboration in the design process. The findings highlighted the opportunity to simplify the cumbersome blending process and identify a straightforward, explicit material for effectively communicating design intentions to generative models.

We propose TypeDance, an authoring tool that empowers both novices and designers with a robust blending technique to create semantic typographic logos from user-customized images. Delighted from “An Image is worth a thousand words” [17, 44] and “Everything you see can be design material” [14, 62], we allow creators to express their design intentions by highlighting the visual representation in their own images. Meanwhile, multiple design inspirations are extracted from the image references for personalizing design. Additionally, we introduce a novel blending technique based on diffusion models that support blending imagery with typeface at all levels of granularity. To guarantee the legibility of both typeface and imagery, we harness a vision-language model to assist creators in pinpointing the position of the generated output within the type-imagery spectrum. We also enable them to edit and refine the output, e.g., making it resemble the typeface “D” more or adopting typewriter-like imagery, as illustrated in Fig. 1. To assess the utility of TypeDance, we conduct the baseline comparison and user study with nine novices and nine designers. Extensive cases and user feedback have revealed the expressiveness of TypeDance in generating a wide range of diverse semantic typographic logos across different scenarios. In summary, we made the following contributions:

(1) A formative study that identifies generalizable design patterns and simulatable design workflow.

(2) An intent-aware input based on user-personalized image that goes beyond ambiguous text prompt, providing a detailed visual description of the desired logo design for generative AI.

(3) A blending technique that seamlessly incorporates imagery with all levels of typeface granularity.

(4) An authoring tool that integrates a comprehensive workflow, empowering creators to ideate, select, generate, evaluate, and iterate their designs.