发表机构
Homiwoo; École Polytechnique（霍米沃; 巴黎综合理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究平面图生成模型的域转移问题，提出用程序方法生成合成训练数据集，通过强制执行物理约束并牺牲建筑现实主义来预训练模型，有效提升零样本跨域性能及微调效率，优于现有方法。
AI 中文摘要
对域转移的鲁棒性是平面图生成模型超越其训练的单个数据集应用的关键要求。由于不同的建筑文化、空间限制和施工实践，平面图在不同地区差异很大，获取新的标注数据集成本高昂且特定于领域。此前尚无工作在条件式平面图生成中研究这种鲁棒性。本文在三个公共数据集上评估了两种根本不同生成范式的最先进模型，发现它们对域转移高度敏感。为以最小目标域监督缓解此问题，引入一种程序方法生成大规模合成训练数据集，该数据集强制执行严格物理约束，同时通过高度不规则空间安排和激进几何扰动牺牲建筑现实主义。结果表明，在该合成数据上预训练可显著提高零样本跨域性能，优于在MagicPlan上的域内训练，还为微调提供高效初始化，在低数据情况下比现实世界初始化基线性能提升高达40%。
英文摘要
Robustness to domain shift is a key requirement for floor plan generative models to be applicable beyond the single dataset they were trained on, as floor plans vary widely across regions due to distinct architectural cultures, spatial constraints, and construction practices, while acquiring new annotated datasets remains costly and domain-specific. Yet, no prior work has studied this robustness in the context of conditioned floor plan generation. In this paper, we evaluate state-of-the-art models from two fundamentally different generative paradigms across three public datasets (RPLAN, MagicPlan and Swiss Dwellings) and show that they are highly sensitive to domain shift, with up to an order of magnitude performance degradation when transferred across domains. To mitigate this with minimal target-domain supervision, we introduce a procedural method to generate a large-scale synthetic training dataset that enforces strict physical constraints (non-overlapping rooms, valid door placement, graph consistency) while intentionally sacrificing architectural realism through highly irregular spatial arrangements and aggressive geometric perturbation of room shapes. We show that pre-training on this synthetic data considerably improves zero-shot cross-domain performance, outperforming in-domain training on MagicPlan. Furthermore, it provides a highly effective initialization for fine-tuning, accelerating target domain adaptation and outperforming real-world initialization baselines by up to 40% in a low-data regime.
