Summary of Typedance: Creating Semantic Typographic Logos From Image Through Personalized Generation, by Shishi Xiao et al.
TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generation
by Shishi Xiao, Liangwei Wang, Xiaojuan Ma, Wei Zeng
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an AI-assisted tool called TypeDance for designing personalized semantic typographic logos that harmoniously blend typeface and imagery to represent semantic concepts. The tool incorporates design rationales with a generative model, allowing for diverse aesthetic designs with flexible control. It leverages combinable design priors extracted from uploaded image exemplars and supports type-imagery mapping at various structural granularity. The authors also instantiate a comprehensive design workflow in TypeDance, including ideation, selection, generation, evaluation, and iteration. User evaluations confirmed the usability of TypeDance in design across different scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TypeDance is an AI tool that helps create cool logo designs. It takes what you want to say (semantic concepts) and combines it with images to make a visually appealing logo. The tool uses AI to generate lots of different designs, so you can pick the one you like best. It also helps designers work more efficiently by automating some parts of the design process. |
Keywords
» Artificial intelligence » Generative model
