A Unified Deep Learning Framework for Real-Time Multimodal Interaction in Immersive VR/AR

Abstract

Virtual and augmented reality (VR/AR) technologies are reshaping the paradigm of human–computer interaction by providing immersive and spatially rich environments. However, the effectiveness of VR/AR interfaces is often constrained by static design choices that fail to adapt to users’ real-time behaviors and cognitive states. This paper proposes an adaptive multimodal framework that integrates gesture, speech, and eye-tracking inputs with contextual awareness to optimize VR/AR interactions. A deep reinforcement learning model is introduced to dynamically adjust interface layouts, input modalities, and feedback mechanisms based on user performance and engagement. The system is validated through experiments on simulated VR tasks, demonstrating improved task efficiency, reduced error rates, and enhanced user satisfaction compared to conventional static interfaces. Our contributions include (1) designing a real-time adaptive interface framework for VR/AR systems, (2) introducing a reinforcement learning–driven optimization mechanism for multimodal input fusion, and (3) providing empirical evidence that adaptive VR/AR interfaces significantly improve user experience in immersive environments.