GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion

Designing functional and aesthetically coherent floor plansrequires exploring a vast space of possible room arrangements, a taskthat quickly becomes overwhelming for human designers. In this pa-per, we propose GRE-Diff, a controllable and interactive diffusion-basedframework that automates the creation and editing of apartment floorplans under user-specified constraints. By combining AI-generated sug-gestions with real-time, human-in-the-loop editing, the system enablesusers to specify room types, room counts, boundary shapes, and editingoperations through LLM-parsed instructions or GUI-based interaction.It then generates a diverse set of plausible and well-structured designs forrefinement. At the core of our approach is Gaussian Room Embedding(GRE), a continuous latent representation that models each room as aspatial Gaussian distribution capturing its location and extent. Exten-sive experiments on the RPLAN dataset show that GRE-Diff produceshigh-quality, constraint-aware, and editable polygonal layouts, offeringa practical step toward bridging AI-driven automation and human cre-ativity in spatial design.