Co-Layout: LLM-driven Co-optimization for Interior Layout

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.

Authors

• Chucheng Xiang University of Science and Technology of China
• Ruchao Bao University of Science and Technology of China
• Biyin Feng Tsinghua University
• Wenzheng Wu University of Science and Technology of China
• Zhongyuan Liu Tencent
• Yirui Guan Tencent
• Ligang Liu University of Science and Technology of China

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