StoryDiffusion: How to Support UX Storyboarding With Generative-AI

作者：Z W Liang, Xiaoyu Zhang, Kevin Ma, Zhao Liu, Xipei Ren, Kosa Goucher-Lambert, Can Liu · 年份：2025 · DOI：10.1145/3716553.3750793 · 被引用次数：7 · 研究领域：Artificial Intelligence in Games、Human Motion and Animation、Digital Humanities and Scholarship

Storyboarding is an established method for designing user experiences. Generative AI can support this process by helping designers quickly create visual narratives. However, existing tools mainly focus on improving the accuracy of text-to-image generation. There is a lack of understanding on how to effectively support the entire creative process of storyboarding and how to develop AI-powered tools to be integrated into designers' diverse workflows. In this work, we designed and developed StoryDiffusion, a system that integrates text-to-text and text-to-image models, to support the generation of narratives and images in a single pipeline. In a user study, we observed 12 UX design students using the system for both concept ideation and illustration tasks. Our findings identified AI-directed vs. user-directed creative strategies in both tasks and revealed the importance of supporting the interchange between narrative iteration and image generation. We also found effects of the design tasks on their strategies and preferences, providing insights for future development. © 2025 Copyright held by the owner/author(s).