用高斯分布建模房间，让AI自动设计可编辑的户型图
GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion
- 用连续高斯分布表示房间位置和范围，实现精准空间建模
- 在RPLAN数据集上生成符合约束的高质量布局，支持实时修改
- 适合室内设计、建筑规划人员快速生成与调整方案
生成功能合理且视觉协调的平面布局需探索海量房间排列组合，对人类设计师而言极具挑战。本文提出GRE-Diff，一种基于扩散模型的可控交互式框架，可在用户指定约束下自动化创建与编辑公寓布局。通过结合AI建议与实时人机协作，用户可通过大语言模型解析指令或图形界面指定房间类型、数量、边界形状及编辑操作，系统生成多样且结构合理的可优化设计方案。核心是高斯房间嵌入（GRE），将每个房间建模为捕捉其位置与范围的空间高斯分布。在RPLAN数据集上的大量实验表明，GRE-Diff能生成高质量、约束感知且可编辑的多边形布局，为实现AI自动化与人类创造力在空间设计中的融合迈出实际一步。
原文摘要 · Abstract (English)
Designing functional and aesthetically coherent floor plans requires exploring a vast space of possible room arrangements, a task that quickly becomes overwhelming for human designers. In this paper, we propose GRE-Diff, a controllable and interactive diffusion-based framework that automates the creation and editing of apartment floor plans under user-specified constraints. By combining AI-generated suggestions with real-time, human-in-the-loop editing, the system enables users to specify room types, room counts, boundary shapes, and editing operations through LLM-parsed instructions or GUI-based interaction. It then generates a diverse set of plausible and well-structured designs for refinement. At the core of our approach is Gaussian Room Embedding (GRE), a continuous latent representation that models each room as a spatial Gaussian distribution capturing its location and extent. Extensive experiments on the RPLAN dataset show that GRE-Diff produces high-quality, constraint-aware, and editable polygonal layouts, offering a practical step toward bridging AI-driven automation and human creativity in spatial design.
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