新论文：ChatHouseDiffusion：提示词引导的建筑平面图生成与编辑方法
论文：ChatHouseDiffusion：提示词引导的建筑平面图生成与编辑方法
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太长不看版
太长不看版
房子的建筑平面图往往需要建筑师和业主来回沟通、不断修改图纸，例如“卧室最好靠近客厅”，“卧室里面再增加一个储藏间”。反复改图成为设计师和业主矛盾的焦点，“完成版”、“最终版”、“最最终版”、“绝对不改版”、“绝对不改版1”、“绝对不改版2”、“最终确定不改版”、“打死不改版”等等名梗成为每位设计师心中永远的痛。
为了满足建筑平面设计和修改的需求，ChatHouseDiffusion融合大语言模型、图神经网络与扩散模型，把用户自然语言表达的设计需求转化为AI设计的输入信息，并提取房间拓扑关系信息，最后自动生成平面图，并支持局部替换和反复迭代修改，大幅提高了设计效率。
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研究背景
研究背景
建筑平面图是建筑设计的起点，也是最需要频繁修改的环节。以往设计中，建筑师需要依赖CAD工具一点点绘制，每次修改都要耗费大量时间。而对于普通业主而言，一般只能提出一些类似“书房采光要好”“卧室要安静”这样的自然语言描述需求，需要和设计师反复沟通和修改才能转化为最终的设计图纸。
房间布局设计调整过程繁琐复杂（本插图由GPT 5生成）
近年来，人工智能已经在平面图生成领域取得不少进展：生成对抗网络方法能自动生成房间布局；扩散模型则让布局生成更稳定、更自然；还有部分研究尝试引入交互功能，比如添加或删除房间。但现存方法仍然存在以下不足：（1）输入门槛高，通常需要用户先画好拓扑关系图，操作复杂；（2）交互能力弱，难以理解自然语言这种更直观的需求表达；（3）局部修改难，修改一个房间，往往会导致整体重新生成，难以保持一致性。针对这些问题，我们提出ChatHouseDiffusion以提高设计效率。
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数据驱动AI在智能优化中的应用
数据驱动AI在智能优化中的应用
ChatHouseDiffusion方法如图所示，主要可以分为两个部分：（1）基于大语言模型对自然语言房间设计条件的理解与解析；（2）基于扩散模型进行建筑平面图的条件生成与编辑。
2.1 设计意图解析
用户只需输入一句话需求，系统就会调用大语言模型，将其转化为JSON格式的结构化信息，清晰记录房间位置、大小和连接关系。
2.2 建筑平面图生成
为了理解房间之间的拓扑关系，本研究引入了Graphormer来捕捉房间之间的拓扑关系，并将其编码后作为设计条件输入扩散模型，在房间外轮廓约束下，扩散模型负责一步步“去噪”，逐渐生成符合需求的平面图。它不仅能保证整体布局合理，还能使细节呈现更为自然逼真
2.3 建筑平面图编辑
在ChatHouseDiffusion中，平面图是通过扩散模型一步步“去噪”生成的，过程里依赖交叉注意力图来确定“卧室”“厨房”等词对应的区域。如果采用传统方法，一旦修改条件（如把卧室挪到北边），就会导致整张图重画，其他房间也被打乱。因此ChatHouseDiffusion在生成时保存注意力图，编辑时在初始去噪阶段沿用原始注意力图，随后再启用修改后的注意力图，从而保证整体布局不受影响。
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方法效果
方法效果
在开源RPLAN数据集上，ChatHouseDiffusion展现出显著优势，其生成房间布局与建筑师设计房间布局基本一致。特别是在对设计输入进行人工解析的情况下，生成结果与真实标注的IoU达到0.8以上，这意味着随着大语言模型的持续进步，ChatHouseDiffusion的效果还会进一步提升。此外，它在生成之外还具有很强的修改能力。添加房间、删除房间、移动位置都能快速高效完成，且整体布局保持稳定，真正实现了人机协作。
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开源平台
开源平台
我们基于ChatHouseDiffusion搭建了一个开源平台，用户只需手绘外轮廓并输入简单的文字需求，系统就能快速生成建筑平面图，并支持后续的交互式编辑。平台集成了绘图、文本输入、自动生成与局部修改等功能，操作直观，轻松易用。相关代码可参考GitHub仓库：https://github.com/ChatHouseDiffusion/chathousediffusion
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结语
结语
ChatHouseDiffusion让建筑平面设计走向了“所言即所得”。从一句话到一张平面图，从整体生成到局部修改，它不仅展示了AI在建筑领域的潜力，也为我们勾勒出一个更高效、更自由的设计未来。
随着大语言模型的不断发展，我们也将继续探索大语言模型在建筑和结构设计中应用的可能性，欢迎各位专家、老师、同学共同交流探讨。
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