HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation
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Computer Science > Machine Learning
Title:HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation
Abstract:In this work, we propose HypergraphFormer, a novel and efficient approach to floor plan generation based on learning hypergraph representations with a large language model (LLM). The model is trained via supervised fine-tuning to generate a hypergraph-based textual representation that encodes spatial relationships and connectivity information within floor plans. We train and evaluate our approach on the RPLAN dataset, and further demonstrate its generalizability on a separate out-of-distribution dataset, which we release in this paper. Our method outperforms state-of-the-art techniques based on rasterized or vectorized representations across a diverse set of metrics. We also show improved data efficiency, particularly under distribution shift. The hypergraph formulation enables the generation of floor plans for arbitrary, irregular, user-specified boundaries by decoupling apartment footprints from their functional and geometric subdivisions. Furthermore, we show that the proposed methodology offers a high degree of editability, making it particularly well suited to design-oriented workflows supported by LLMs.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.18932 [cs.LG] |
| (or arXiv:2605.18932v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18932
arXiv-issued DOI via DataCite (pending registration)
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