Abstract
Text-to-image models are enabling efficient design space exploration, rapidly generating images from text prompts. However, many generative AI tools are imperfect for product design applications as they are not built for the goals and requirements of product design. The unclear link between text input and image output further complicates their application. This work empirically investigates design space exploration strategies that can successfully yield product images that are feasible, novel, and aesthetic, which are three common goals in product design. Specifically, user actions within the global and local editing modes, including their time spent, prompt length, mono vs. multi-criteria prompts, and goal orientation of prompts, are analyzed. Key findings reveal the pivotal role of mono vs. multi-criteria and goal orientation of prompts in achieving specific design goals over time and prompt length. The study recommends prioritizing the use of multi-criteria prompts for feasibility and novelty during global editing, while favoring mono-criteria prompts for aesthetics during local editing. Overall, this paper underscores the nuanced relationship between the AI-driven text-to-image models and their effectiveness in product design, urging designers to carefully structure prompts during different editing modes to better meet the unique demands of product design.
Abstract (translated)
文本转图像模型正在实现高效的设计空间探索，并迅速生成基于文本提示的图像。然而，许多生成人工智能工具并不适合产品设计应用，因为它们不是为产品设计的目标和要求而设计的。文本输入与图像输出的不明确联系进一步复杂了它们的应用。本研究通过实证研究探讨了能够成功生成可行、新颖和美观的产品图像的设计空间探索策略，这是产品设计中常见的三种目标。具体来说，研究分析用户在全局和局部编辑模式下的操作，包括他们的时间、提示长度、单条件 vs. 多条件提示以及提示的目标方向。关键发现表明，单条件 vs. 多条件提示和提示的目标导向在实现特定设计目标方面起着关键作用，而提示的长度对目标的影响更加显著。研究建议在全局编辑模式中优先使用多条件提示以实现可行性和新颖性，而在局部编辑模式中优先使用单条件提示以实现美观性。总之，本文强调了AI驱动的文本转图像模型与产品设计之间的复杂关系，呼吁设计师在不同的编辑模式下仔细构建提示，更好地满足产品设计的独特需求。
URL
https://arxiv.org/abs/2408.03946
