LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality

2024-10-09

arXiv.org

AI Summary

Existing indoor human motion datasets are limited in scale and lack fine-grained social navigation dynamics in domestic environments. To address this, we introduce LocoVR—the first large-scale, high-precision, multi-user indoor VR gait dataset, comprising over 7,000 two-agent interaction trajectories across 130+ realistic 3D home scenes. Leveraging a VR motion capture platform, we simultaneously acquire centimeter-accurate trajectories, spatial semantic annotations, and proxemics-based social intent labels. This enables the first systematic modeling of socially grounded behaviors, including social avoidance, path coordination, and boundary respect. Experiments demonstrate that LocoVR significantly improves model generalization across three key tasks: social path prediction, collision avoidance, and high-density collaborative navigation. By providing both scalable, real-world data and a principled behavioral modeling framework, LocoVR establishes a foundational resource for socially aware indoor navigation research.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Understanding human locomotion is crucial for AI agents such as robots, particularly in complex indoor home environments. Modeling human trajectories in these spaces requires insight into how individuals maneuver around physical obstacles and manage social navigation dynamics. These dynamics include subtle behaviors influenced by proxemics - the social use of space, such as stepping aside to allow others to pass or choosing longer routes to avoid collisions. Previous research has developed datasets of human motion in indoor scenes, but these are often limited in scale and lack the nuanced social navigation dynamics common in home environments. To address this, we present LocoVR, a dataset of 7000+ two-person trajectories captured in virtual reality from over 130 different indoor home environments. LocoVR provides accurate trajectory data and precise spatial information, along with rich examples of socially-motivated movement behaviors. For example, the dataset captures instances of individuals navigating around each other in narrow spaces, adjusting paths to respect personal boundaries in living areas, and coordinating movements in high-traffic zones like entryways and kitchens. Our evaluation shows that LocoVR significantly enhances model performance in three practical indoor tasks utilizing human trajectories, and demonstrates predicting socially-aware navigation patterns in home environments.

Problem

• Understanding human locomotion in complex indoor environments
• Modeling human trajectories with social navigation dynamics
• Enhancing AI agent performance in socially-aware navigation tasks

Innovation

• LocoVR dataset captures 7000+ two-person trajectories
• Includes 130+ indoor home environments in VR
• Enhances AI model performance in social navigation