Human-operational 3D indoor layout generation with LLM-driven anthropometric simulation

Three-dimensional (3D) indoor layout generation is essential for interior design automation, digital twin construction, human behavior simulation, and occupant activity modeling. Although recent layout-generation methods have achieved significant advances in visual realism, they have overlooked anthropometric considerations, such as human scale and behaviors, producing layouts unsuitable for human interactions and unusable for activities in physical and virtual environments. This study introduces a human-operational 3D indoor layout-generation approach driven by a large language model–based anthropometric simulation. The proposed pipeline employs a human-action predictor that associates atomic actions with objects, and a spatial rule generator combining the scene context, predicted actions, and anthropometric data to ensure clearance, accessibility, and alignment. These anthropometric spatial rules are integrated into a diffusion-based scene-graph model during training and inference. This method reduces anthropometric rule violations, supporting the automated generation of human-operational spaces in which occupants can perform activities in digital twins and real-world implementations. • LLM automatically generates human actions and anthropometric spatial rules. • Diffusion-based scene generation model incorporates human operational space. • Proposed method enhances spatial validity while maintaining generative performance. • Method ensures human-operational 3D layout generation based on anthropometric design principles.