Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis

2025-08-04

AI Summary

Real-time, physically plausible human–avatar interaction motion synthesis remains a critical bottleneck in VR/AR and humanoid robotics, as existing approaches fail to simultaneously satisfy responsiveness, physical feasibility, and safety. This paper introduces the first end-to-end real-time reactive diffusion planner that jointly synthesizes interaction motions and corresponding reaction forces, integrated with an agent-aware reinforcement learning locomotion policy to enable context-aware, interpenetration-free, and naturally synchronized dynamic interactions. Our method unifies autoregressive reactive diffusion modeling, physics-constrained optimization, and real-time human-factor motion modeling. Evaluations on Inter-X and InterHuman benchmarks demonstrate significant improvements in motion quality and interaction coherence, achieving state-of-the-art performance. The system has been successfully deployed in a VR-based human–robot collaboration interface, validating its practical efficacy and real-time capability with sub-30-ms latency.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Real-time synthesis of physically plausible human interactions remains a critical challenge for immersive VR/AR systems and humanoid robotics. While existing methods demonstrate progress in kinematic motion generation, they often fail to address the fundamental tension between real-time responsiveness, physical feasibility, and safety requirements in dynamic human-machine interactions. We introduce Human-X, a novel framework designed to enable immersive and physically plausible human interactions across diverse entities, including human-avatar, human-humanoid, and human-robot systems. Unlike existing approaches that focus on post-hoc alignment or simplified physics, our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner, ensuring seamless synchronization and context-aware responses. To enhance physical realism and safety, we integrate an actor-aware motion tracking policy trained with reinforcement learning, which dynamically adapts to interaction partners' movements while avoiding artifacts like foot sliding and penetration. Extensive experiments on the Inter-X and InterHuman datasets demonstrate significant improvements in motion quality, interaction continuity, and physical plausibility over state-of-the-art methods. Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction, showcasing its potential for advancing human-robot collaboration.

Problem

• Real-time physically plausible human interaction synthesis
• Balancing real-time responsiveness with physical feasibility
• Ensuring safety in dynamic human-machine interactions

Innovation

• Real-time auto-regressive reaction diffusion planner
• Actor-aware motion tracking with reinforcement learning
• Dynamic adaptation to interaction partners' movements