用自然语言生成灵活住宅布局，支持多种输入方式。
Text Semantics to Flexible Design: A Residential Layout Generation Method Based on Stable Diffusion Model
- 融合自然语言与控制网络，实现多模态约束下的布局生成。
- 在房间面积或连接信息不全时仍保持生成灵活性。
- 适合设计师和普通用户直接表达设计需求。
基于稳定扩散模型的跨模态设计方法，解决传统住宅布局生成中灵活性不足的问题。该方法支持边界、布局及自然语言等多种输入形式，引入ControlNet实现稳定生成，通过知识图谱将设计经验转化为自然语言，提升可解释性。实验表明，在多模态约束下，即使缺少房间面积或连接关系的精确信息，本方法生成效果仍优于现有最先进模型，显著增强设计灵活性与可控性。
原文摘要 · Abstract (English)
Flexibility in the AI-based residential layout design remains a significant challenge, as traditional methods like rule-based heuristics and graph-based generation often lack flexibility and require substantial design knowledge from users. To address these limitations, we propose a cross-modal design approach based on the Stable Diffusion model for generating flexible residential layouts. The method offers multiple input types for learning objectives, allowing users to specify both boundaries and layouts. It incorporates natural language as design constraints and introduces ControlNet to enable stable layout generation through two distinct pathways. We also present a scheme that encapsulates design expertise within a knowledge graph and translates it into natural language, providing an interpretable representation of design knowledge. This comprehensibility and diversity of input options enable professionals and non-professionals to directly express design requirements, enhancing flexibility and controllability. Finally, experiments verify the flexibility of the proposed methods under multimodal constraints better than state-of-the-art models, even when specific semantic information about room areas or connections is incomplete.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。