用自然语言交互生成编辑户型图，支持精准局部修改。
ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans
- 结合大模型理解指令，图神经网络建模布局关系，扩散模型生成图纸。
- 在户型匹配度上优于现有方法，局部修改无需重做全图。
- 适合建筑师快速试错，也适合非专业人士参与设计。
户型图的生成与编辑在建筑设计中至关重要，需兼顾灵活性与效率。现有方法依赖大量输入信息，难以实现对用户修改的交互式响应。本文提出ChatHouseDiffusion，利用大语言模型（LLMs）解析自然语言指令，通过Graphormer编码空间拓扑关系，并结合扩散模型实现灵活的户型生成与编辑。该方法支持基于用户想法的迭代设计调整，显著提升设计效率。实验表明，相较于现有模型，该方法在交并比（IoU）指标上表现更优，可实现精确的局部修改而无需整体重绘，更具实用性。结果还显示，模型不仅能严格遵循用户需求，还通过交互能力使设计过程更加直观。
原文摘要 · Abstract (English)
The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency. Existing methods demand extensive input information and lack the capability for interactive adaptation to user modifications. This paper introduces ChatHouseDiffusion, which leverages large language models (LLMs) to interpret natural language input, employs graphormer to encode topological relationships, and uses diffusion models to flexibly generate and edit floor plans. This approach allows iterative design adjustments based on user ideas, significantly enhancing design efficiency. Compared to existing models, ChatHouseDiffusion achieves higher Intersection over Union (IoU) scores, permitting precise, localized adjustments without the need for complete redesigns, thus offering greater practicality. Experiments demonstrate that our model not only strictly adheres to user specifications but also facilitates a more intuitive design process through its interactive capabilities.
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