Collaborative work in social virtual reality often requires an interplay of loosely coupled collaboration from different virtual locations and tightly coupled face-to-face collaboration. Without appropriate system mediation, however, transitioning between these phases requires high navigation and coordination efforts. In this paper, we present an interaction system that allows collaborators in virtual reality to seamlessly switch between different collaboration models known from related work.
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January 2024
This article examines the choices between sitting and standing in virtual reality (VR) experiences, addressing conflicts, challenges, and opportunities. It explores issues such as the risk of motion sickness in stationary users and virtual rotations, the formation of mental models, consistent authoring, affordances, and the integration of embodied interfaces for enhanced interactions. Furthermore, it delves into the significance of multisensory integration and the impact of postural mismatches on immersion and acceptance in VR.
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December 2024
The inquiry into the impact of diverse transitions between cross-reality environments on user experience remains a compelling research endeavor. Existing work often offers fragmented perspectives on various techniques or confines itself to a singular segment of the reality-virtuality spectrum, be it virtual reality or augmented reality. This study embarks on bridging this knowledge gap by systematically assessing the effects of six prevalent transitions while users remain immersed in tasks spanning both virtual and physical domains.
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December 2023
Continuous locomotion in VR provides uninterrupted optical flow, which mimics real-world locomotion and supports path integration . However, optical flow limits the maximum speed and acceleration that can be effectively used without inducing cybersickness. In contrast, teleportation provides neither optical flow nor acceleration cues, and users can jump to any length without increasing cybersickness.
View Article and Find Full Text PDFModeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks.
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