Seeing My Future: Predicting Situated Interaction Behavior in Virtual Reality

2025-10-12

AI Summary

This work addresses the critical challenge of human intent understanding and future behavior prediction in VR/AR systems. We propose a hierarchical, intent-aware dynamic graph convolutional network (GCN) framework that uniquely integrates cognition-driven intent modeling into dynamic GCNs. The method jointly learns high-level user motivations and fine-grained actions—such as gaze direction and object interaction—while incorporating historical human pose sequences and scene context to model the spatiotemporal evolution of human–environment interactions. Evaluated on real-world benchmark datasets and an in-the-loop real-time VR environment, our approach achieves significant improvements over state-of-the-art methods in prediction accuracy, temporal consistency, and cross-scenario generalizability. It establishes a novel paradigm for building proactive, adaptive intelligent VR/AR systems capable of anticipating user behavior.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Virtual and augmented reality systems increasingly demand intelligent adaptation to user behaviors for enhanced interaction experiences. Achieving this requires accurately understanding human intentions and predicting future situated behaviors - such as gaze direction and object interactions - which is vital for creating responsive VR/AR environments and applications like personalized assistants. However, accurate behavioral prediction demands modeling the underlying cognitive processes that drive human-environment interactions. In this work, we introduce a hierarchical, intention-aware framework that models human intentions and predicts detailed situated behaviors by leveraging cognitive mechanisms. Given historical human dynamics and the observation of scene contexts, our framework first identifies potential interaction targets and forecasts fine-grained future behaviors. We propose a dynamic Graph Convolutional Network (GCN) to effectively capture human-environment relationships. Extensive experiments on challenging real-world benchmarks and live VR environment demonstrate the effectiveness of our approach, achieving superior performance across all metrics and enabling practical applications for proactive VR systems that anticipate user behaviors and adapt virtual environments accordingly.

Problem

• Predicting human intentions and situated behaviors in VR
• Modeling cognitive processes driving human-environment interactions
• Developing proactive VR systems that anticipate user actions

Innovation

• Hierarchical intention-aware framework models cognitive mechanisms
• Dynamic Graph Convolutional Network captures human-environment relationships
• Predicts fine-grained situated behaviors from historical dynamics