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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2502.09867 |
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| _version_ | 1866910076317466624 |
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| author | Tao, Sirui Liang, Ivan Peng, Cindy Wang, Zhiqing Palani, Srishti Dow, Steven P. |
| author_facet | Tao, Sirui Liang, Ivan Peng, Cindy Wang, Zhiqing Palani, Srishti Dow, Steven P. |
| contents | 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 implications for AI-based product design support tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design Tao, Sirui Liang, Ivan Peng, Cindy Wang, Zhiqing Palani, Srishti Dow, Steven P. Human-Computer Interaction Artificial Intelligence H.5.2 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 implications for AI-based product design support tools. |
| title | DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design |
| topic | Human-Computer Interaction Artificial Intelligence H.5.2 |
| url | https://arxiv.org/abs/2502.09867 |