Advancing Interior Design with AI: Controllable Stable Diffusion for Panoramic Image Generation
Year: 2025
Type: article
Abstract: AI-driven technologies have significantly advanced panoramic image generation in interior design; however, existing methods often lack controllability and consistency in rendering high-quality, coherent panoramas. To address these limitations, the study proposes CSD-Pano, a controllable and stable diffusion framework tailored for panoramic interior design generation. The study also introduces PSD-4, a curated dataset of panoramic scenes covering diverse interior decoration styles to support training and evaluation. CSD-Pano enables fine-grained control over aesthetic attributes, layout coherence, and stylistic consistency. Furthermore, the study designs a panoramic loss function that enhances spatial coherence, geometric alignment, and perceptual fidelity. Extensive qualitative and quantitative experiments demonstrate that CSD-Pano achieves superior performance compared to existing baselines, with significant improvements in SSIM and LPIPS metrics. These results validate the effectiveness of our approach in advancing automated panoramic interior design.
Source: Buildings
Authors: Wanggong Yang, Congcong Wang, Luxiang Liu, Shuying Dong, Yifei Zhao
Institution: Beijing Institute of Graphic Communication
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4409634836