Co-Layout: LLM-driven Co-optimization for Interior Layout
This addresses interior design automation for users needing efficient and high-quality layout generation, but it is incremental as it builds on existing methods like LLMs and integer programming.
The paper tackles automated interior design by combining large language models with grid-based integer programming to jointly optimize room layout and furniture placement, resulting in significantly better solution quality and computational efficiency compared to existing two-stage pipelines.
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.