A Novel Immersive VR Game Model for Recontextualization in Virtual Environments: The μVRModel

Abstract

In recent years, immersive VR had a great boost in terms of adoption and research perspectives, especially those regarding the serious gaming universe. Within the cultural heritage field, virtual re-contextualization of items is a crucial task to be accomplished by individuals to understand a 3D reconstructed environment as a whole and to assign a meaning and a value to a specific cultural object. Immersive VR and consumer HMDs still present several issues related to motion sickness and locomotion: the interest in real-walking techniques outperforming other locomotion methods is growing year by year, although limited by physical constraints, higher costs, or current technology. In this work, we propose a novel game model (µVR) that combines real-walking techniques and an adaptive, game-driven, multi-scale progression to craft immersive re-contextualization applications. The presented model aims to minimize motion sickness while fully exploiting the physical tracked area and augmenting the understanding of what the user is experiencing at different world scales. We define and formalize the µVR model and its components mathematically for the sake of reproducibility, then we present results from a pilot test planned to validate the model on real users. Results assure the usability and effectiveness of VR model even if further implementation needs to be done.

Digital Cultural Heritage

“…It is an example of AR opening up a complete set of new possibilities for CH, including the virtual transformation of physical elements. "A Novel Immersive VR Game Model for Recontextualization in Virtual Environments: The µVRModel" [18] puts forward a new model for VR games, named µVR. By combining real-walking techniques and an adaptive, game-driven, multi-scale progression, it allows the creation of immersive VR applications targeting re-contextualization activities that require users to relocate virtual cultural objects in their former locations.…”

HUMANNET—A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction

“…A lot of applications that would benefit from real-time object reconstruction such as self-driving cars [5,6], interactive medium particularly virtual reality [7] (VR) and video games, augmented reality [8] (AR) and extended reality [9] (XR). Furthermore, depth sensor information can improve gesture [10,11] and posture recognition [12] technologies as these tasks generally have a lot of important depth information embedded into them.…”

Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data

“…Quite a few real world applications would benefit greatly from depth sensors and/or real-time object reconstruction, starting with collision avoidance in autonomous vehicles [ 10 , 11 ], robotics [ 12 , 13 ], or posture recognition [ 14 , 15 ]. Other object reconstruction applications may involve interactive medium, like obstacle avoidance in virtual reality [ 16 , 17 ], augmented reality [ 18 ], extended reality [ 19 ], and more. Even though 3D object reconstruction opens up a lot of possibilities to various fields, the main issue with object reconstruction is that it generally requires either intricate camera setups or moving the camera around the object in order to scan the entirety of the object and to build its full profile from all sides.…”