用统一语法生成多样户型，支持多种条件输入
Unified Vector Floorplan Generation via Markup Representation
- 设计统一标记语言FML，将布局转为结构化语法
- 单模型在RPLAN上超越多个专用模型性能
- 适合需要多条件户型生成的设计师与系统
自动生成住宅平面图长期是连接建筑与计算机图形学的核心挑战，旨在提升空间设计效率与可及性。早期基于约束满足或组合优化的方法虽能保证可行性，但缺乏多样性与灵活性。近期生成模型虽取得良好效果，却难以泛化到异构条件任务（如基于场地边界、房间邻接图或部分布局生成），原因在于其表征方式不佳。为此，我们提出楼层平面标记语言（FML），一种将平面信息编码于单一结构化语法中的通用表示，将整个平面图生成问题转化为下一个词预测任务。基于FML，我们构建了基于Transformer的生成模型FMLM，可在多种条件下生成高质量且功能合理的平面图。在RPLAN数据集上的全面实验表明，尽管仅使用单一模型，FMLM仍超越此前针对特定任务的最先进方法。
原文摘要 · Abstract (English)
Automatic residential floorplan generation has long been a central challenge bridging architecture and computer graphics, aiming to make spatial design more efficient and accessible. While early methods based on constraint satisfaction or combinatorial optimization ensure feasibility, they lack diversity and flexibility. Recent generative models achieve promising results but struggle to generalize across heterogeneous conditional tasks, such as generation from site boundaries, room adjacency graphs, or partial layouts, due to their suboptimal representations. To address this gap, we introduce Floorplan Markup Language (FML), a general representation that encodes floorplan information within a single structured grammar, which casts the entire floorplan generation problem into a next token prediction task. Leveraging FML, we develop a transformer-based generative model, FMLM, capable of producing high-fidelity and functional floorplans under diverse conditions. Comprehensive experiments on the RPLAN dataset demonstrate that FMLM, despite being a single model, surpasses the previous task-specific state-of-the-art methods.
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