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

TL;DR

This paper introduces a behavior-aware 3D scene generation framework that incorporates anthropometric data and VLM analysis to create indoor layouts optimized for human activity and usability.

Contribution

It presents a novel method combining vision-language models with anthropometric constraints to generate human-centric indoor scenes, validated through technical and human studies.

Findings

Improved task completion times in generated scenes

Enhanced trajectory efficiency for users

Better human-object manipulation space

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.