Text Semantics to Flexible Design: A Residential Layout Generation Method Based on Stable Diffusion Model
This addresses the problem of generating flexible residential layouts for professionals and non-professionals, though it appears incremental as it builds on existing Stable Diffusion and ControlNet methods.
The paper tackles the challenge of inflexibility in AI-based residential layout design by proposing a cross-modal approach using the Stable Diffusion model, which allows users to specify boundaries and layouts with natural language constraints and achieves better flexibility under multimodal constraints than state-of-the-art models.
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.