Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how the space is used. Yet in real interior design, a layout is judged by how well it supports its occupants, e.g., their activities and physical needs. We introduce Function2Scene, a framework for generating 3D indoor layouts from functional specifications, i.e., natural-language design briefs describing who will use a room and what they need to do there. Given such a specification, our system parses occupant personas and activities, derives a customized set of functional design constraints from a taxonomy of 17 criteria spanning spatial, ergonomic, activity, and environmental considerations, and uses these constraints to guide layout generation. Rather than relying on an LLM to directly produce a final scene, Function2Scene performs iterative evaluation and refinement through a tool-augmented check-and-repair loop, combining geometric measurements, LLM-based contextual reasoning, and VLM-based visual assessment. Experiments on 30 professionally written interior-design cases show that Function2Scene produces layouts that better satisfy functional requirements than recent LLM-based scene synthesis baselines, with our results preferred in 94.3% of pairwise comparisons. Our work reframes text-driven indoor scene synthesis from placing plausible objects to designing spaces that support human use.

翻译：大多数基于文本的三维室内场景合成方法通过以物体为中心的提示生成房间，侧重于放置哪些家具而非空间如何使用。然而在实际室内设计中，布局的优劣取决于其对居住者的支持程度，例如其活动与生理需求。本文提出Function2Scene框架，能够根据功能规格（即描述房间使用者及其活动需求的自然语言设计简报）生成三维室内布局。给定此类规格后，系统解析用户角色与活动，从涵盖空间、人体工学、活动及环境因素的17项分类标准中推导出定制化功能设计约束，并通过这些约束指导布局生成。该框架不依赖大语言模型直接生成最终场景，而是通过工具增强的检测-修复循环进行迭代评估与优化，结合几何测量、基于大语言模型的上下文推理以及基于视觉语言模型的视觉评估。在30个专业室内设计案例上的实验表明，Function2Scene生成的布局在满足功能需求方面优于近期基于大语言模型的场景合成基线，其成果在94.3%的成对比较中获得更优评价。本研究将文本驱动的室内场景合成从放置合理物体重新定义为设计支持人类使用的空间。