DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design

要旨

Generative AI has enabled novice designers to quickly create professional-looking visual representations for product concepts. However, novices have limited domain knowledge that could constrain their ability to write prompts that effectively explore a product design space. To understand how experts explore and communicate about design spaces, we conducted a formative study with 12 experienced product designers and found that experts — and their less-versed clients — often use visual references to guide co-design discussions rather than written descriptions. These insights inspired DesignWeaver, an interface that helps novices generate prompts for a text-to-image model by surfacing key product design dimensions from generated images into a palette for quick selection. In a study with 52 novices, DesignWeaver enabled participants to craft longer prompts with more domain-specific vocabularies, resulting in more diverse, innovative product designs. However, the nuanced prompts heightened participants' expectations beyond what current text-to-image models could deliver. We discuss the implications of AI-based product design support tools.

著者
Sirui Tao
University of California San Diego, La Jolla, California, United States
Ivan Liang
UCSD, San Diego, California, United States
Cindy Peng
University of California, San Diego, La Jolla, California, United States
Zhiqing Wang
University of California San Diego, La Jolla, California, United States
Srishti Palani
Salesforce, Palo Alto, California, United States
Steven P.. Dow
University of California, San Diego, La Jolla, California, United States
DOI

10.1145/3706598.3714211

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714211

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Exploring Physical and Digital Product Design

G418+G419
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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