Behavior-Aware Anthropometric Scene Generation for Human-Usable 3D Layouts

2026-03-03

AI Summary

This work proposes a behavior-aware anthropometric framework for 3D scene generation that addresses the common oversight of human behavioral needs in existing methods, which often fail to ensure spatial comfort and functionality. By integrating the behavioral reasoning capabilities of vision-language models (VLMs) with personalized anthropometric data, the approach translates behavior–object relationships into parametric layout constraints tailored to individual body dimensions. The resulting human-centered 3D layouts demonstrate strong geometric plausibility and are validated through user perception studies (N=16). Furthermore, real-scale experiments involving both individuals (N=20) and groups (N=18) show significant improvements in task completion time, walking trajectory efficiency, and the ergonomic fit of human–object interaction spaces.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: 3D Computer Vision

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Well-designed indoor scenes should prioritize how people can act within a space rather than merely what objects to place. However, existing 3D scene generation methods emphasize visual and semantic plausibility, while insufficiently addressing whether people can comfortably walk, sit, or manipulate objects. To bridge this gap, we present a Behavior-Aware Anthropometric Scene Generation framework. Our approach leverages vision-language models (VLMs) to analyze object-behavior relationships, translating spatial requirements into parametric layout constraints adapted to user-specific anthropometric data. We conducted comparative studies with state-of-the-art models using geometric metrics and a user perception study (N=16). We further conducted in-depth human-scale studies (individuals, N=20; groups, N=18). The results showed improvements in task completion time, trajectory efficiency, and human-object manipulation space. This study contributes a framework that bridges VLM-based interaction reasoning with anthropometric constraints, validated through both technical metrics and real-scale human usability studies.

Problem

• 3D scene generation
• human behavior
• anthropometric constraints
• spatial usability
• human-object interaction

Innovation

• Behavior-Aware Scene Generation
• Anthropometric Constraints
• Vision-Language Models
• Human-Usable 3D Layouts
• Parametric Layout Constraints