用大模型+数学规划，一键生成符合人体工学的室内布局
Co-Layout: LLM-driven Co-optimization for Interior Layout
- 大模型提取文本需求，转为网格化设计约束
- 联合优化布局与家具摆放，提升空间利用率和可达性
- 分阶段求解策略，效率比传统方法快得多
我们提出一种新型自动化室内设计框架，结合大语言模型（LLMs）与基于网格的整数规划，联合优化房间布局与家具摆放。给定文本提示后，由大模型驱动的智能体工作流提取与房间配置、家具排列相关的结构化设计约束，并将其编码为受「Modulor」启发的统一网格表示。该方法考虑了通道连通性、房间可达性、空间独占性及用户偏好等关键设计要求。为提升计算效率，采用从粗到细的优化策略：先在低分辨率网格上求解简化问题，再引导全分辨率求解。在多种场景下的实验表明，该联合优化方法在方案质量上显著优于现有两阶段设计流程，且通过分阶段策略实现显著的计算效率提升。
原文摘要 · Abstract (English)
We present a novel framework for automated interior design that combines large language models (LLMs) with grid-based integer programming to jointly optimize room layout and furniture placement. Given a textual prompt, the LLM-driven agent workflow extracts structured design constraints related to room configurations and furniture arrangements. These constraints are encoded into a unified grid-based representation inspired by ``Modulor". Our formulation accounts for key design requirements, including corridor connectivity, room accessibility, spatial exclusivity, and user-specified preferences. To improve computational efficiency, we adopt a coarse-to-fine optimization strategy that begins with a low-resolution grid to solve a simplified problem and guides the solution at the full resolution. Experimental results across diverse scenarios demonstrate that our joint optimization approach significantly outperforms existing two-stage design pipelines in solution quality, and achieves notable computational efficiency through the coarse-to-fine strategy.
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