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[2602.22507] Space Syntax-guided Post-training for Residential Floor Plan Generation
Summary
This paper introduces Space Syntax-guided Post-training (SSPT) for enhancing residential floor plan generation by integrating architectural principles into generative models.
Why It Matters
The research addresses limitations in existing generative models that often overlook critical architectural features. By incorporating space syntax theory, this approach aims to improve the functionality and usability of generated floor plans, making it relevant for architects and AI developers alike.
Key Takeaways
- SSPT integrates architectural knowledge into generative models for better floor plan design.
- Two strategies for SSPT—iterative retraining and reinforcement learning—show significant improvements in public space dominance.
- The introduction of SSPT-Bench provides a standardized evaluation framework for assessing model performance.
Computer Science > Machine Learning arXiv:2602.22507 (cs) [Submitted on 26 Feb 2026] Title:Space Syntax-guided Post-training for Residential Floor Plan Generation Authors:Zhuoyang Jiang, Dongqing Zhang View a PDF of the paper titled Space Syntax-guided Post-training for Residential Floor Plan Generation, by Zhuoyang Jiang and Dongqing Zhang View PDF HTML (experimental) Abstract:Pre-trained generative models for residential floor plans are typically optimized to fit large-scale data distributions, which can under-emphasize critical architectural priors such as the configurational dominance and connectivity of domestic public spaces (e.g., living rooms and foyers). This paper proposes Space Syntax-guided Post-training (SSPT), a post-training paradigm that explicitly injects space syntax knowledge into floor plan generation via a non-differentiable oracle. The oracle converts RPLAN-style layouts into rectangle-space graphs through greedy maximal-rectangle decomposition and door-mediated adjacency construction, and then computes integration-based measurements to quantify public space dominance and functional hierarchy. To enable consistent evaluation and diagnosis, we further introduce SSPT-Bench (Eval-8), an out-of-distribution benchmark that post-trains models using conditions capped at $\leq 7$ rooms while evaluating on 8-room programs, together with a unified metric suite for dominance, stability, and profile alignment. SSPT is instantiated with two strategies: (i) iterati...