Locomotion in CAVE: Enhancing Immersion through Full-Body Motion

TL;DR

This paper presents a novel locomotion framework for CAVE virtual environments that enhances immersion and reduces motion sickness by using optimized human motion recognition technology.

Contribution

It introduces a new locomotion method combining camera calibration and action recognition to improve user experience in CAVE systems.

Findings

Significant improvement in perceived realness and self-presence.

Effective reduction of motion sickness during virtual navigation.

Validated through user study comparing traditional and proposed methods.

Abstract

Cave Automatic Virtual Environment (CAVE) is one of the virtual reality (VR) immersive devices currently used to present virtual environments. However, the locomotion methods in the CAVE are limited by unnatural interaction methods, severely hindering the user experience and immersion in the CAVE. We proposed a locomotion framework for CAVE environments aimed at enhancing the immersive locomotion experience through optimized human motion recognition technology. Firstly, we construct a four-sided display CAVE system, then through the dynamic method based on Perspective-n-Point to calibrate the camera, using the obtained camera intrinsics and extrinsic parameters, and an action recognition architecture to get the action category. At last, transform the action category to a graphical workstation that renders display effects on the screen. We designed a user study to validate the effectiveness of our method. Compared to the traditional methods, our method has significant improvements in realness and self-presence in the virtual environment, effectively reducing motion sickness.