Space Syntax-guided Post-training for Residential Floor Plan Generation

Abstract

Residential floor plan generation requires not only geometric fidelity but also spatial configurational logic: shared living spaces should be integrative, while private spaces should remain segregated. Existing generators increasingly use room-relation graphs as input-side conditions, but generated layouts are rarely evaluated on the output side for configurational quality, and such evaluation is rarely fed back into model optimization. We propose Space Syntax-guided Post-training (SSPT), a framework that turns space-syntax integration from a post-hoc analysis tool into a computable feedback signal for already-trained floor plan generators. SSPT introduces the Space Syntax Integration Oracle (SSIO), which converts generated layouts into rectangle-space graphs and measures public-space dominance and functional hierarchy. SSIO is first applied to real residential data to establish empirical configurational references, then connected to two SSPT strategies: SSPT-Iter, a basic generate-filter-retrain route, and SSPT-PPO, the first RL-based post-training route for floor plan generation. We also introduce SSPT-Bench, a new evaluation system for measuring the output-side spatial configurational quality of post-trained generators under an out-of-distribution setting. Experiments show that both strategies improve public-space dominance and functional-hierarchy alignment over the unpost-trained baseline. SSPT-PPO achieves stronger gains, lower variance, and higher efficiency than iterative retraining. These results show that output-side configurational evaluation can serve as actionable post-training feedback, offering a practical path for injecting architectural theory into existing floor plan generation backbones.