Odor visualization translates odor information and perception into visual outcomes and arouses the corresponding olfactory synesthesia, surpassing the spatial limitation that odors can only be perceived where they are present. Traditional odor visualization has typically relied on unidimensional mappings, such as odor-to-color associations, and has required extensive manual design efforts. However, the advent of generative AI (Gen AI) and large language models (LLMs) presents a new opportunity for automatic odor visualization. Nonetheless, gaps remain in bridging olfactory perception with generative tools to produce odor images. To address these gaps, this paper introduces Paint by Odor, a pipeline that leverages Gen AI and LLMs to transform olfactory perceptions into rich, aesthetically engaging visual representations. Two experiments were conducted, where 30 participants smelled real-world odors and provided descriptive data and 28 participants evaluated 560 generated odor images through seven systematically designed prompts. Our findings explored the capability of LLMs in producing olfactory perception by comparing it with human responses and revealed the underlying mechanisms and effects of language-based descriptions and several abstraction styles on odor visualization. Our work further discussed the possibility of automatic odor visualization without human participation. These explorations and results have bridged the research gap in odor visualization using LLMs and Gen AI, offering valuable design insights and various possibilities for future applications.

翻译：气味可视化将气味信息与感知转化为视觉呈现，激发相应的嗅觉联觉，从而突破气味仅能在其存在处被感知的空间限制。传统气味可视化通常依赖于单维映射（如气味-颜色关联），且需要大量人工设计投入。然而，生成式人工智能（Gen AI）与大语言模型（LLMs）的出现为自动化气味可视化提供了新机遇。当前研究在连接嗅觉感知与生成工具以产出气味图像方面仍存在空白。为填补这一空白，本文提出"气味作画"（Paint by Odor）流程，利用Gen AI与LLMs将嗅觉感知转化为丰富且具有美学吸引力的视觉表征。我们开展了两项实验：第一项实验邀请30名参与者嗅闻真实气味并提供描述性数据；第二项实验由28名参与者通过七种系统设计的提示词对560幅生成气味图像进行评估。研究通过对比LLMs与人类反应，探索了LLMs生成嗅觉感知的能力，并揭示了基于语言的描述及多种抽象风格对气味可视化的内在机制与影响。本文进一步探讨了无需人工参与的自动化气味可视化可能性。这些探索与成果填补了基于LLMs与Gen AI的气味可视化研究空白，为未来应用提供了宝贵的设计见解与多样化可能性。