Table of Links Introduction


Related Work
2.1 Semantic Typographic Logo Design
2.2 Generative Model for Computational Design
2.3 Graphic Design Authoring Tool


Formative Study
3.1 General Workflow and Challenges
3.2 Concerns in Generative Model Involvement
3.3 Design Space of Semantic Typography Work


Design Consideration


Typedance and 5.1 Ideation
5.2 Selection
5.3 Generation
5.4 Evaluation
5.5 Iteration


Interface Walkthrough and 6.1 Pre-generation stage
6.2 Generation stage
6.3 Post-generation stage


Evaluation and 7.1 Baseline Comparison
7.2 User Study
7.3 Results Analysis
7.4 Limitation


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


Conclusion and References Introduction Introduction Introduction Related Work
2.1 Semantic Typographic Logo Design
2.2 Generative Model for Computational Design
2.3 Graphic Design Authoring Tool Related Work 2.1 Semantic Typographic Logo Design 2.1 Semantic Typographic Logo Design 2.2 Generative Model for Computational Design 2.2 Generative Model for Computational Design 2.3 Graphic Design Authoring Tool 2.3 Graphic Design Authoring Tool Formative Study
3.1 General Workflow and Challenges
3.2 Concerns in Generative Model Involvement
3.3 Design Space of Semantic Typography Work Formative Study Formative Study 3.1 General Workflow and Challenges 3.1 General Workflow and Challenges 3.2 Concerns in Generative Model Involvement 3.2 Concerns in Generative Model Involvement 3.3 Design Space of Semantic Typography Work 3.3 Design Space of Semantic Typography Work Design Consideration Design Consideration Design Consideration Typedance and 5.1 Ideation
5.2 Selection
5.3 Generation
5.4 Evaluation
5.5 Iteration Typedance and 5.1 Ideation Typedance and 5.1 Ideation 5.2 Selection 5.2 Selection 5.3 Generation 5.3 Generation 5.4 Evaluation 5.4 Evaluation 5.5 Iteration 5.5 Iteration Interface Walkthrough and 6.1 Pre-generation stage
6.2 Generation stage
6.3 Post-generation stage Interface Walkthrough and 6.1 Pre-generation stage Interface Walkthrough and 6.1 Pre-generation stage 6.2 Generation stage 6.2 Generation stage 6.3 Post-generation stage 6.3 Post-generation stage Evaluation and 7.1 Baseline Comparison
7.2 User Study
7.3 Results Analysis
7.4 Limitation Evaluation and 7.1 Baseline Comparison Evaluation and 7.1 Baseline Comparison 7.2 User Study 7.2 User Study 7.3 Results Analysis 7.3 Results Analysis 7.4 Limitation 7.4 Limitation 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 Discussion 8.1 Personalized Design: Intent-aware Collaboration with AI 8.1 Personalized Design: Intent-aware Collaboration with AI 8.2 Incorporating Design Knowledge into Creativity Support Tools 8.2 Incorporating Design Knowledge into Creativity Support Tools 8.3 Mix-User Oriented Design Workflow 8.3 Mix-User Oriented Design Workflow Conclusion and References Conclusion and References Conclusion and References 3.2 Concerns in Generative Model Involvement With the rise of generative models like Midjourney, many designers began to embrace AI for design assistance. The following are two main concerns about AI involvement in their design process. Lack of controllability of text-conditioned generative model. The most widely used method for controlling generative models is through textual prompts. Though various tools can assist in designing prompts, like PromptBase[1] and PromptHero[2], these tools emphasize imitating certain styles and lacking precise control of specific shape and layout. When dealing with highly personalized imagery, the time and effort invested in crafting a prompt experiences a significant surge. As E1 notes, “Each time I intricately design a prompt in anticipation of the generated result, it prompts me to open Illustrator again.” Lack of controllability of text-conditioned generative model. The most widely used method for controlling generative models is through textual prompts. Though various tools can assist in designing prompts, like PromptBase[1] and PromptHero[2], these tools emphasize imitating certain styles and lacking precise control of specific shape and layout. When dealing with highly personalized imagery, the time and effort invested in crafting a prompt experiences a significant surge. As E1 notes, “Each time I intricately design a prompt in anticipation of the generated result, it prompts me to open Illustrator again.” Lack of controllability of text-conditioned generative model. “Each time I intricately design a prompt in anticipation of the generated result, it prompts me to open Illustrator again.” Lack of refinement and editability of generative result. Lack of editability in the results appears to be a common issue of generated models. While users may be generally satisfied with the overall outcome, there can be instances where certain details may not meet their expectations (e.g., dislike colors, redundant objects). One approach to address this is to regenerate the entire image, which results in losing the current design. Lack of refinement and editability of generative result. Lack of editability in the results appears to be a common issue of generated models. While users may be generally satisfied with the overall outcome, there can be instances where certain details may not meet their expectations (e.g., dislike colors, redundant objects). One approach to address this is to regenerate the entire image, which results in losing the current design. Lack of refinement and editability of generative result. 3.3 Design Space of Semantic Typography Work To further identify design patterns shaping semantic typographic logos, we collect and analyze a corpus of 427 real-world examples[3]. To ensure the diversity of sources, we include examples from prior research [23, 61], reputable design communities[4], and influential design shared on social media. The keywords we mainly use for search are “topographic logo,” “semantic topography,” and “word as image”. Focusing on logogram (Chinese (97), Japanese (34), Korea (30)) and alphabet language (English (229), French(20), Russian(17)), we filter search engine results for each language based on “popular” and “new” criteria, considering both widespread acknowledgment and timeliness. As Fig. 3 shows, our corpus analysis reveals two critical aspects of design patterns: (1) typeface granularity and (2) type-imagery mapping. “topographic logo,” “semantic topography,” “word as image” “popular” “new” 3.3.1 Typeface Granularity. While various languages have their own unique symbols, they conform to a shared structural granularity. This hierarchy, arranged from local to global, encompasses stroke, letter, and multi-letter, respectively*.* 3.3.1 Typeface Granularity. stroke letter multi-letter, • Stroke-level. It often employs stroke decomposition, where a single stroke or a group of strokes is associated with imagery (123/427 examples). As illustrated in Fig. 3 (b1), the spout of a teapot aligns with the original curve in the letter “g”, enhancing semantic expression while preserving typeface integrity. As the smallest unit of typeface, stroke-level blend can be implemented multiple times within a typeface to enrich visual representation. • Stroke-level. • Letter-level. Individual letters are commonly used in our collected corpus (189/427 examples). Rather than conducting a letter-level blend to every single letter, certain examples tend to focus on partially representative letters within a word, particularly the first letter in Fig. 3 (b6). To emphasize the imagery, they employ techniques such as scaling, elongation, and rotation on the letters. • Letter-level. • Multi-letter-level. Blending imagery with multiple letters or entire words is the multi-letter-level blend (115/427 examples). It regards the typeface as a cohesive unit and spatially arranges the letter in a proper position. According to Fig. 3 (b5), the letters are rearranged and distorted to create the recognizable silhouette of a dog. • Multi-letter-level. 3.3.2 Type-Imagery Mapping. We observe a complex linkage between typeface and imagery. Prior works simplify such linkage by adopting a single mapping strategy, where one letter is associated with specific imagery. To approximate TypeDance to the design practice of semantic typographic logo, we manually encode the corpus and identify three typical mapping patterns: one-to-one, one-to-many, many-to-one. With a comprehensive understanding of how typeface interact with imagery, we can better instantiate the design principles to empower the creation process. 3.3.2 Type-Imagery Mapping. one-to-one one-to-many many-to-one • One-to-One Mapping. One logo corresponds to one imagery commonly observed in the corpus (294/427 examples). It preserves typeface structures using partial strokes or letters to represent the same imagery. For instance, in Fig. 3 (b1), the letter “g” incorporates the imagery of a teapot spout. Additionally, we observed that logos with one-to-one mapping often employ repetitive imagery with a consistent style within a particular typeface. • One-to-One Mapping. • One-to-Many Mapping. The semantic typographic logo in this portion will distribute multiple imageries in the typeface involved in the design (14/427 examples). This mapping type supports rich imagery coverage within a compact space, where the semantic concepts usually share the same theme. Fig. 3 (b4) integrates both spoon and fork into a letter “m”, underscoring the theme of the meal. • One-to-Many Mapping. • Many-to-One Mapping. Another aspect involves integrating multiple letters in typeface into a single imagery (119/427 examples), typically achieved by combining entire words to convey a complex visual representation of meaning, see Fig. 3 (b3, b4). This creative approach can be traced back to Giuseppe Arcimboldo, who skillfully merged various elements and shapes to create cohesive portraits and figures [32]. The many-to-one mapping enhances the overall unity and deepens the expression of semantic meaning. • Many-to-One Mapping. 3.3.3 Summary. Through corpus analysis, we uncover different typeface granularity and various type-imagery mapping. The combination of these design patterns presents an opportunity to convey rich visual representations. Both logograms and alphabet languages adhere to these patterns but exhibit distinct preferences. The complex typeface structure of logograms and their inherent pictorial origins result in more elaborate imagery combinations. For instance, compared to the French word in Fig. 3 (b5), the Chinese words in Fig.3 (b6) achieve a blending with traditional landscape paintings without spatially rearranging the typefaces. 3.3.3 Summary. Additionally, we note distinctions in real-world logo design compared to the formal definition of semantic typography. In real-world logo design, blending is not uniformly applied to all typefaces. Instead, emphasis is placed on letters in the initial position or those closely related semantically to the imagery (171/427 examples). Moreover, in significant designs, the typeface often has a less direct semantic relationship with the incorporated imagery (203/427 examples). This observation aligns with insights from an interview with E2, who remarked, “The selection of imagery depends on the brand’s story, and the logo’s meaning is for users to associate the imagery directly with the brand.” 4 DESIGN CONSIDERATION The expert interview reveals that personalizing a semantic typographic logo relies on blending specific typefaces (e.g., at different granularity) and imagery (e.g., concrete visual representation), further identified through corpus analysis. Challenges in user workflow and concern about generative AI highlight using easily accessible images for effective personalization, eliminating the need for intricate text prompts that may not fully capture user intentions. Guided by Shneiderman’s design principle [46] of “Design with low thresholds, high ceilings, and wide walls,” our goal is to develop a tool that enables novices to create with accessible materials and interactions (D1), automates complex blend manipulation for professionals (D2), and integrates essential functionalities for a streamlined design process (D3). The roadmap we derive the design considerations is illustrated in Fig. 2. Below, we present the set of design considerations: “Design with low thresholds, high ceilings, and wide walls, D1 D2 D3 D1. Intent-aware design material and interaction. We aim to support flexible material selection, allowing the easy switch between typefaces at different granularities and the selection of imagery from specific visual representations in a user-customized image. D1. Intent-aware design material and interaction. D2. Facilitate the professional generation process. We aim to propose an automatic blending approach that supports typefaces at all levels of granularity, ensuring harmonious and diverse designs. D2. Facilitate the professional generation process. automatic blending approach D3. Provide necessary functionalities to support a comprehensive workflow. In the pre-generation stage, we will incorporate an ideation module for brainstorming. In the post-generation stage, an evaluation and iteration module will be added to identify, edit, and refine the generated result within the type-imagery spectrum. D3. Provide necessary functionalities to support a comprehensive workflow. Authors:
(1) SHISHI XIAO, The Hong Kong University of Science and Technology (Guangzhou), China;
(2) LIANGWEI WANG, The Hong Kong University of Science and Technology (Guangzhou), China;
(3) XIAOJUAN MA, The Hong Kong University of Science and Technology, China;
(4) WEI ZENG, The Hong Kong University of Science and Technology (Guangzhou), China. Authors: Authors (1) SHISHI XIAO, The Hong Kong University of Science and Technology (Guangzhou), China; (2) LIANGWEI WANG, The Hong Kong University of Science and Technology (Guangzhou), China; (3) XIAOJUAN MA, The Hong Kong University of Science and Technology, China; (4) WEI ZENG, The Hong Kong University of Science and Technology (Guangzhou), China. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. available on arxiv available on arxiv [3] The online page of a corpus of semantic typographic logo [4] https://www.pinterest.com/

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Why Your AI-Generated Logos Still Feel Off

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

73 Top Saudi Sites Were Analyzed for Font Morphology, Image Design and User Preferences

Boost Your YouTube CTR with This Smart, Simple Thumbnail Generator

TypeDance: An AI-Assisted Workflow for Semantic Typographic Logos

This AI Tool Lets You Design Logos That Actually Mean Something

Logo Design Techniques No One Talks About (Until Now)

Create Custom Logos Using TypeDance’s 5-Step Process

73 Top Saudi Sites Were Analyzed for Font Morphology, Image Design and User Preferences

Boost Your YouTube CTR with This Smart, Simple Thumbnail Generator

TypeDance: An AI-Assisted Workflow for Semantic Typographic Logos

This AI Tool Lets You Design Logos That Actually Mean Something

Logo Design Techniques No One Talks About (Until Now)

Create Custom Logos Using TypeDance’s 5-Step Process

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps