# Function2Scene：基于功能描述的3D室内布局生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-29 08:00
- AIHOT 分数：56
- AIHOT 链接：https://aihot.virxact.com/items/cmpulkg6204vgslagq0qkzz2a
- 原文链接：https://arxiv.org/abs/2605.30819

## AI 摘要

Function2Scene是一个从自然语言功能描述（如用户需求和活动）生成3D室内布局的框架。与传统基于物品提示的方法不同，它将设计问题重构为空间功能支持。系统解析用户画像和活动，并基于包含空间、人体工学等17项标准的功能约束分类体系来指导布局生成。其核心是采用检查-修复循环进行迭代优化，结合几何测量、大语言模型的上下文推理与视觉语言模型的视觉评估。实验在30个专业设计案例上表明，其布局在功能需求满足度上显著优于近期基线，在配对比较中偏好率达94.3%。

## 正文

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.