GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion

• URL: http://arxiv.org/abs/2607.08086v1
• Date: Thu, 09 Jul 2026 03:41:54 GMT
• Title: GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion
• Abstract summary: We propose GRE-Diff, a controllable and interactive diffusion-based framework that automates the creation and editing of apartment floor plans.<n>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.<n>It then generates a diverse set of plausible and well-structured designs for refinement.
• Score: 12.298384256265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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