Room Adjacency Extraction for High-Accuracy Floorplan Generation from Natural Language Descriptions

This paper presents a deep generative model for automatically extracting room adjacency relationships from natural language descriptions, aiming to enhance the accuracy of automated floorplan generation. The proposed approach ex-tends the Transformer encoder-decoder architecture by integrating textual information via Adaptive Layer Normalization (AdaLN) and incorporates a Graph Attention Network-based Variational Autoencoder (GAT-VAE) to effectively capture global topological relationships among rooms. Experimental results demonstrate that this model achieves superior perfor-mance in extracting accurate room adjacencies significantly surpassing existing methods based on large language models (LLM). Further experiments confirm that floorplans generated using adjacency graphs extracted by the proposed method yield higher spatial consistency compared to those derived from LLM, as measured by IoU metrics. These findings con-firm that accurate adjacency extraction plays a critical role in floorplan synthesis and demonstrate the effectiveness of combining structured latent modeling and text-conditioned generation.