用逐间生成方式模拟建筑师设计流程，提升方案迭代灵活性。
FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction
- 借鉴语言模型的逐词预测思路，按顺序生成房间布局。
- 在文本生成平面图任务中表现媲美扩散模型与Tell2Design。
- 适合需要分步修改、渐进式设计的智能建筑辅助场景。
在建筑设计过程中，平面图生成本质上是逐步推进和反复迭代的。然而，现有的平面图生成模型多为端到端一次性生成像素级布局，这一范式往往与真实建筑实践中渐进式工作流不兼容。为此，我们受大语言模型中自回归‘下一词预测’机制启发，提出一种面向建筑平面图建模的新型‘下一房间预测’范式。实验评估表明，FPDS在文本到平面图任务中表现与扩散模型及Tell2Design相当，显示出其在未来智能建筑设计中的应用潜力。
原文摘要 · Abstract (English)
In the architectural design process, floor plan generation is inherently progressive and iterative. However, existing generative models for floor plans are predominantly end-to-end generation that produce an entire pixel-based layout in a single pass. This paradigm is often incompatible with the incremental workflows observed in real-world architectural practice. To address this issue, we draw inspiration from the autoregressive 'next token prediction' mechanism commonly used in large language models, and propose a novel 'next room prediction' paradigm tailored to architectural floor plan modeling. Experimental evaluation indicates that FPDS demonstrates competitive performance in comparison to diffusion models and Tell2Design in the text-to-floorplan task, indicating its potential applicability in supporting future intelligent architectural design.
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