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

TL;DR

This paper introduces Co-Layout, a framework that leverages large language models and grid-based optimization to automate interior layout design, improving solution quality and computational efficiency.

Contribution

It presents a novel integration of LLMs with grid-based integer programming for joint interior layout and furniture placement optimization.

Findings

Outperforms existing two-stage pipelines in solution quality

Achieves significant computational efficiency with coarse-to-fine optimization

Successfully handles diverse interior design scenarios

Abstract

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.