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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2602.22507 |
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| _version_ | 1866910210572943360 |
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| author | Jiang, Zhuoyang Zhang, Dongqing |
| author_facet | Jiang, Zhuoyang Zhang, Dongqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22507 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Space Syntax-guided Post-training for Residential Floor Plan Generation Jiang, Zhuoyang Zhang, Dongqing Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Space Syntax-guided Post-training for Residential Floor Plan Generation |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.22507 |