Volumetric video (VV) is an emerging 3D format that allows the integration of real people into XR (extended reality) applications. Recent cost-effective AI-based methods have enabled VV capture using single handheld cameras or mobile phones. This study addresses the quality, integration, and acceptance of AI-based VV content creation in an augmented reality (AR) application designed to enhance museum experiences. The main result reveals that, although the current VV quality is lower than professional standards, users still find significant added value and enjoy its immersive experience.

Redirected walking (RDW) allows users to explore a virtual environment larger than the physical tracking space by real walking. When RDW is applied within certain thresholds, users do not notice it and remain immersed in the virtual environment. There are many factors that affect these thresholds such as walking speed, gender, and field of view. However, redirected walking thresholds (RDTs) were never studied when an avatar is present, and it is not known how the sense of embodiment over this avatar affects users’ RDTs.

Currently, the most popular text input method in VR is Controller Pointing (CP). While this method is easy and intuitive to use, it requires users to have steady hands, and the overlaying of the keyboard onto the virtual scene occludes a part of the scene. In this work, we proposed two new text input methods: Sector Input and T9VR , that utilize a circular keyboard that is attached to the HTC Vive controller. A preliminary study with 24 subjects was conducted to explore the potential of the proposed methods, in comparison with CP. While CP performed significantly better than the proposed methods in terms of objective typing speed and error rate, T9VR was able to match with CP in a number of subjective measures.

To allow users to perform real walking in a virtual environment larger than the physical space, redirected walking (RDW) techniques could be employed. Users do not notice this manipulation and immersion remains intact when RDW is applied within certain thresholds. Although many studies on RDW detection thresholds exists, in none of these studies, users were performing an additional task during the threshold identification process. These existing thresholds could be only conservative estimates and the potential of RDW may not be fully utilized.

Redirected Walking (RDW) allows users to perform real walking in virtual worlds that are larger than the available physical space. Many RDW algorithms rely on the prediction of users’ possible paths in the virtual environment (VE) to calculate where users should be redirected to. This prediction could be obtained from the structure of the VE, where users look, or from existing path models. In this work, we examine users’ walking behaviors in the presence of a virtual agent acting as a tour guide. Results showed that users changed their speed significantly to match the agent’s walking speed. Furthermore, users also tend to adapt their trajectories to match with the agent’s path.

To enable real walking in a virtual environment (VE) that is larger than the available physical space, redirection techniques that introduce multisensory conflicts between visual and nonvisual cues to manipulate different aspects of a user's trajectory could be applied. When applied within certain thresholds, these manipulations could go unnoticed and immersion remains intact. Research effort has been spent on identifying these thresholds and a wide range of thresholds was reported in different studies. These differences in thresholds could be explained by many factors such as individual differences, walking speed, or context settings such as environment design, cognitive load, distractors, etc.

For the contest held at IEEE VR 2018 3DUIs for three tasks have to be designed. The tasks include climbing a ladder, fly a drone in first-person perspective and stacking objects. The goal of our design is to find an easy yet intuitive solution to the given tasks. In that way, for the ladder task, a 3DUI is implemented closely mimicking a real ladder ascent or descent using only the controllers to interact with the environment. For the drone flying, we propose a solution which uses the head orientation to control the drone. The third task concluded with a no gravity, temporary storage space which is used to prepare the final stack that can be released with a trigger press.

Redirected walking (RDW) refers to a number of techniques that enable users to explore a virtual environment larger than the real physical space. These techniques are based on the introduction of a mismatch in rotation, translation and curvature between the virtual and real trajectories, quantified as rotational, translational and curvature gains. When these gains are applied within certain thresholds, the manipulation is unnoticeable and immersion is maintained. Existing studies on RDW thresholds reported a wide range of threshold values. These differences could be attributed to many factors such as individual differences, walking speed, or environment settings. In this paper, we propose a study to investigate one of the environment settings that could potentially influence curvature RDW thresholds: the environment size. The detailed description of the study is also provided, where the adaptive, 2-alternative forced choice method is used to identify the detection thresholds.

Moving through a virtual environment (VE) by real walking is beneficial to user immersion, feeling of presence and way finding. However, the available physical spaces are of limited size and usually much smaller than the VE. One solution to this problem is using redirection techniques (RDTs). While the focus of existing research has been mostly on continuous RDTs, work on discrete RDTs is still limited.

Redirected Walking Techniques (RWTs) enable a user to immersively explore a virtual environment larger than the available physical space by real walking. RWTs are based on the use of gains (translational, rotational and curvature), which introduce a mismatch between the virtual and physical trajectories. When these gains are applied within certain thresholds, the "manipulation" is unnoticeable and immersion is maintained. Numerous research has been carried out to identify these thresholds and factors that affect them such as walking speed, environment structure or tasks involved. However, it has not been known whether users change their walking behavior when RWTs are applied and if this in turn influences ftheir perception thresholds.

Redirected walking refers to a number of techniques that enable users to explore a virtual environment larger than the physical space by real walking. The efficiency of these techniques has been shown to improve when predictions about the user's future trajectory are incorporated. Predictions can be made not only based on the knowledge about the environment but also on how humans behave in it. In a maze-like environment, it is known that most animal species show a strong preference to alternate their turning direction. This is called spontaneous alternation behavior (SAB). Although such behavior has also been observed in humans during maze tracing tasks, little is known whether they also exhibit this behavior during real walking, and if they do, what their alternation rate is.

Redirected walking (RDW) allows users to interact with a Virtual Environment (VE) much larger than the real space. However, there is a limit to how much a user can be redirected such that immersion in the VE is still preserved. While much effort has been spent on identifying this limit, so-called redirected walking thresholds, these thresholds are more a general guideline than being personalized for each user. Since individuals' differences in perception play an important role in users's experience in a redirected VE, there is potential to optimize the thresholds using these differences.