DAVE: Deep Learning-Based Asymmetric Virtual Environment for Immersive Experiential Metaverse Content
Yunsik Cho, Seunghyun Hong, Mingyu Kim, Jinmo Kim
Abstract
In this study, we design an interface optimized for the platform by adopting deep learning in an asymmetric virtual environment where virtual reality (VR) and augmented reality (AR) users participate together. We also propose a novel experience environment called deep learning-based asymmetric virtual environment (DAVE) for immersive experiential metaverse content. First, VR users use their real hands to intuitively interact with the virtual environment and objects. A gesture interface is designed based on deep learning to directly link gestures to actions. AR users interact with virtual scenes, objects, and VR users via a touch-based input method in a mobile platform environment. A text interface is designed using deep learning to directly link handwritten text to actions. This study aims to propose a novel asymmetric virtual environment via an intuitive, easy, and fast interactive interface design as well as to create metaverse content for an experience environment and a survey experiment. This survey experiment is conducted with users to statistically analyze and investigate user interface satisfaction, user experience, and user presence in the experience environment.