用自然语言生成真实感香味，可实时迭代优化。
AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models
- 基于多模态大模型，将文本/图像转为12种基础香料组合。
- 用户反馈后香味与真实食物相似度达8/10，人工感接近真实。
- 适合沉浸式体验、健康疗愈和新型人机交互研究者。
嗅觉与食物、记忆及社交体验密切相关，长期激励研究者将嗅觉融入交互系统。然而，多数嗅觉界面仍受限于固定香囊和预设生成模式，且大规模嗅觉数据集的匮乏进一步制约了基于AI的方法。我们提出AromaGen，一个由AI驱动的可穿戴装置，能够从自由文本或视觉输入中实时生成通用香味。AromaGen基于多模态大模型，利用潜在嗅觉知识将语义输入映射为12种精心选择的基础气味成分的结构化混合，并通过颈戴式释放器释放。用户可通过自然语言反馈，借助上下文学习进行迭代优化。在一项受控用户研究中（N=26），AromaGen在零样本生成中达到人类创作混合物水平，并在迭代优化后显著超越，与真实食物香味的中位相似度达8/10，感知人工感降低至与真实食物相当水平。AromaGen是迈向现实世界交互式香味生成的重要一步，为沟通、福祉与沉浸式技术开辟新可能。
原文摘要 · Abstract (English)
Smell's deep connection with food, memory, and social experience has long motivated researchers to bring olfaction into interactive systems. Yet most olfactory interfaces remain limited to fixed scent cartridges and pre-defined generation patterns, and the scarcity of large-scale olfactory datasets has further constrained AI-based approaches. We present AromaGen, an AI-powered wearable interface capable of real-time, general-purpose aroma generation from free-form text or visual inputs. AromaGen is powered by a multimodal LLM that leverages latent olfactory knowledge to map semantic inputs to structured mixtures of 12 carefully selected base odorants, released through a neck-worn dispenser. Users can iteratively refine generated aromas through natural language feedback via in-context learning. Through a controlled user study ($N = 26$), AromaGen matches human-composed mixtures in zero-shot generation and significantly surpasses them after iterative refinement, achieving a median similarity of 8/10 to real food aromas and reducing perceived artificiality to levels comparable to real food. AromaGen is a step towards real-world interactive aroma generation, opening new possibilities for communication, wellbeing, and immersive technologies.
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