House Layout Generation via Diffusion Model with Relative Room Area Ranking

Junbin Xiang, Boyu Hou, Xuefeng Chen, Hongtuo Qi, Liang Feng, Jiepeng Liu, Xianneng Li

Abstract: House layout plays a crucial role in housing planning and design. In recent years, automated generation of house layouts has gained significant attention. The objective is to automatically generate floorplans that meet specific requirements under given constraints. In this paper, we present an extension and improvement of the existing diffusion model, focusing on the generation of housing layouts in more complex constrained scenarios. Firstly, we introduce the consideration of relative room area ranking as a new problem scenario. Secondly, we propose an indirect encoding approach that represents the ranking relationship of room relative areas as a directed graph to address potential issues arising from embedding ranking features. Finally, we introduce a corresponding attention module to capture the newly added constraint relationships. Moreover, we evaluate the proposed approach using a range of metrics and the RPLAN dataset. Our proposed method demonstrates improved accuracy in considering variations in room sizes and provides more reasonable layout. The obtained results also indicate that our enhanced approach exhibits better compatibility and Spearman correlation for relative room area ranking compared to the state-of-the-art methods.