Automated floor plan generation aims to create residential layouts by arranging rooms within a given boundary, balancing topological, geometric, and aesthetic considerations. The existing methods typically use a multi-step pipeline with intermediate representations to decompose the prediction process into several sub-tasks, limiting model flexibility and imposing predefined solution paths. This often results in unreasonable outputs when applied to data unsuitable for these predefined paths, making it challenging for these methods to match human designers, who do not restrict themselves to a specific set of design workflows. To address these limitations, we introduce CE2EPlan, a controllable end-to-end topology- and geometry-enhanced diffusion model that removes restrictions on the generative process of AI design tools. Instead, it enables the model to learn how to design floor plans directly from data, capturing a wide range of solution paths from input boundaries to complete layouts. Extensive experiments demonstrate that our method surpasses all existing approaches using the multi-step pipeline, delivering higher-quality results with enhanced user control and greater diversity in output, bringing AI design tools closer to the versatility of human designers.

翻译：自动平面图生成旨在通过给定边界内布置房间来创建住宅布局，平衡拓扑、几何与美学考量。现有方法通常采用多步骤流水线及中间表示，将预测过程分解为若干子任务，这限制了模型灵活性并强加了预定义的解决方案路径。当应用于不适合这些预定义路径的数据时，该方法常产生不合理输出，使其难以匹敌不受特定设计工作流程限制的人类设计师。为克服这些局限，我们提出CE2EPlan——一种可控的端到端拓扑与几何增强扩散模型，它消除了AI设计工具生成过程的限制。该模型能够直接从数据中学习如何设计平面图，捕捉从输入边界到完整布局的广泛解决方案路径。大量实验表明，我们的方法超越了所有采用多步骤流水线的现有方法，通过增强的用户控制和更高的输出多样性提供更高质量的结果，使AI设计工具更接近人类设计师的多功能性。