A Semantic-Conditional GAN Framework for Structure-Preserving and Controllable Interior Style Generation

Interior style generation requires expressive visual transformation while preserving spatial layout, object boundaries, and semantic relationships. Existing generative and style-transfer methods have improved indoor scene synthesis, but they often suffer from boundary drift, furniture deformation, texture leakage, and unstable style representation when stronger style signals are introduced. To address this problem, this study proposes a semantic-conditional generative adversarial network framework for structure-preserving and controllable interior style generation. The framework constructs semantic masks, boundary maps, and semantic embeddings from indoor images and injects these semantic priors into the generator feature stream through a multi-scale conditional mechanism. A style encoding network is further introduced to represent interior style characteristics and to modulate image-level visual appearance features, including texture patterns, color tones, material-like surface appearance, and illumination-related visual cues, in a controllable manner. The model was trained and evaluated using 5000 selected indoor images from SUN RGB-D and ADE20K, together with a self-constructed style reference set of 600 images covering modern minimalist, Nordic, industrial, and neoclassical interiors. Across baseline comparisons and ablation analyses, the proposed framework achieved a semantic region consistency score of 0.91, maintained higher semantic boundary consistency under increasing style intensity, obtained style consistency scores ranging from 0.88 to 0.90, and achieved an LPIPS score of 0.128. A subjective evaluation with 20 participants also showed higher ratings for realism, style expression, and structural consistency. These results provide quantitative and perceptual evidence that semantic-conditional injection can improve the balance between spatial-semantic preservation and controllable image-level style expression in AI-assisted interior visualization.