{"ID":2835152,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00406","arxiv_id":"2512.00406","title":"GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework","abstract":"Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPlanner, an energy- and functionality-aware generative framework that unifies design evaluation and generation. It consists of a labeled Design Feasibility Dataset for learning constraint priors; a fast Practical Design Evaluator (PDE) for predicting energy performance and spatial-functional validity; a Green Plan Dataset (GreenPD) derived from PDE-guided filtering to pair user requirements with regulation-compliant layouts; and a GreenFlow generator trained on GreenPD with PDE feedback for controllable, regulation-aware generation. Experiments show that GreenPlanner accelerates evaluation by over $10^{5}\\times$ with $\u003e$99% accuracy, eliminates invalid samples, and boosts design efficiency by 87% over professional architects.","short_abstract":"Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of a...","url_abs":"https://arxiv.org/abs/2512.00406","url_pdf":"https://arxiv.org/pdf/2512.00406v1","authors":"[\"Pengyu Zeng\",\"Yuqin Dai\",\"Jun Yin\",\"Jing Zhong\",\"Ziyang Han\",\"Chaoyang Shi\",\"ZhanXiang Jin\",\"Maowei Jiang\",\"Yuxing Han\",\"Shuai Lu\"]","published":"2025-11-29T09:35:50Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}