{"ID":6267023,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-12T03:28:36.982449166Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08086","arxiv_id":"2607.08086","title":"GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.08086","url_pdf":"https://arxiv.org/pdf/2607.08086v1","authors":"[\"Jing Wang\",\"Haoran Xiong\",\"Zihao Yan\",\"Minglun Gong\",\"Hui Huang\"]","published":"2026-07-09T03:41:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Large Language Model\"]","has_code":false}