用大模型学习超图表示，实现可编辑的任意边界户型生成
HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation
- 基于大语言模型学习超图表示，编码空间关系与连接信息
- 在RPLAN及外部数据集上优于现有栅格/矢量方法，尤其在分布外数据上表现更优
- 支持用户自定义不规则边界，适合需频繁修改的设计流程
本文提出HypergraphFormer，一种基于大语言模型（LLM）学习超图表示的新颖高效户型生成方法。模型通过监督微调，生成编码空间关系与连通性信息的超图文本表示。我们在RPLAN数据集上训练并评估该方法，并进一步在论文发布的独立分布外数据集上验证其泛化能力。该方法在多种指标上超越基于栅格或矢量表示的最先进技术，尤其在分布外场景下表现出更强的数据效率。超图建模使户型可针对任意不规则用户指定边界生成，且将住宅轮廓与其功能与几何细分解耦。此外，该方法具备高度可编辑性，特别适合由大模型支持的设计工作流。
原文摘要 · Abstract (English)
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.
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