Interstellar Arc is an immersive sci-fi experience that uses recent advances in headset tech to break new ground in virtual reality. And it all happens inside an empty-looking room in Las Vegas. It feels like I've been transported into a scene straight out of a science fiction movie. I'm walking around on a giant centrifuge in space, which I can see the outlines of at the edge of my vision. Beyond it, I see the planet we're orbiting. The pathways I walk on stretch endlessly above and below me, giving me the feeling I'm in an absolutely massive structure.

As virtual reality (VR) devices become increasingly integrated into everyday settings, a growing number of users without prior experience will engage with VR systems. Automatically detecting a user's familiarity with VR as an interaction medium enables real-time, adaptive training and interface adjustments, minimizing user frustration and improving task performance. In this study, we explore the automatic detection of VR familiarity by analyzing hand movement patterns during a passcode-based door-opening task, which is a well-known interaction in collaborative virtual environments such as meeting rooms, offices, and healthcare spaces. While novice users may lack prior VR experience, they are likely to be familiar with analogous real-world tasks involving keypad entry. We conducted a pilot study with 26 participants, evenly split between experienced and inexperienced VR users, who performed tasks using both controller-based and hand-tracking interactions. Our approach uses state-of-the-art deep classifiers for automatic VR familiarity detection, achieving the highest accuracies of 92.05% and 83.42% for hand-tracking and controller-based interactions, respectively. In the cross-device evaluation, where classifiers trained on controller data were tested using hand-tracking data, the model achieved an accuracy of 78.89%. The integration of both modalities in the mixed-device evaluation obtained an accuracy of 94.19%. Our results underline the promise of using hand movement biometrics for the real-time detection of user familiarity in critical VR applications, paving the way for personalized and adaptive VR experiences.

Abrupt resolution changes in virtual reality (VR) streaming can significantly impair the quality-of-experience (QoE) of users, particularly during transitions from high to low resolutions. Existing QoE models and transmission schemes inadequately address the perceptual impact of these shifts. To bridge this gap, this article proposes, for the first time, an innovative physiological signal-driven QoE modeling and optimization framework that fully leverages users' electroencephalogram (EEG), electrocardiogram (ECG), and skin activity signals. Integrated the proposed QoE framework into the radio access network (RAN) via a deep reinforcement learning (DRL) framework, adaptive transmission strategies have been implemented to allocate radio resources dynamically, which mitigates short-term channel fluctuations and adjusts frame resolution in response to channel variations caused by user mobility. By prioritizing long-term resolution while minimizing abrupt transitions, the proposed solution achieves an 88.7% improvement in resolution and an 81.0% Experimental results demonstrate the effectiveness of this physiological signal-driven strategy, underscoring the promise of edge AI in immersive media services. While this technology enables unprecedented engagement in applications ranging from event viewing to interactive education, its reliance on wireless transmission poses critical challenges. The uncompressed data rates exceeding 1 Gbps and latency thresholds below 20 ms impose stringent demands on network infrastructure, particularly in mobile scenarios where channel fluctuations and user mobility degrade service consistency. Traditional quality of service (QoS) metrics (e.g., bandwidth, jitter, and packet loss) provide necessary but insufficient insights into user satisfaction, necessitating perceptual quality of experience (QoE) frameworks tailored to user's unique requirements [3]-[5].

HTC Vive is following up its intriguing, yet expensive, XR Elite headset with something that's still quite pricey, the 999 Focus Vision. Built on the same platform as the standalone Vive Focus 3, the upgraded model adds a slew of new features like built-in eye tracking, 16MP stereo color front-facing cameras for mixed reality and automatic IPD adjustment (which makes it easier to share). And with the additional 149 DisplayPort wired streaming kit, gamers can also hook the Focus Vision up to their PCs for more intensive VR experiences. But that's to be expected. While Meta has poured tens of billions into making its Quest headsets cheaper and more accessible, without any need to worry about profitability, HTC Vive has leaned towards making more expensive headsets better suited for business and government work.

This post is about deep learning applications for creating better virtual reality experiences. So, let's begin with introduction. Deep learning models are being used in several ways to improve the quality and interactivity of virtual reality experiences. Image Generation: Deep learning models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), can be used to generate realistic and high-quality images and textures for virtual environments. These models can learn to generate images that match the visual characteristics of real-world environments or objects, and create more immersive and engaging VR experiences.

Extended reality technologies aren't new, but the way that we interact with them has changed rapidly within the last several years. As businesses compete for supremacy in this space, the landscape of these new innovations has become confusing and uncertain for many. What is the Metaverse, where is it taking us next, and what are the key technologies driving its development? Before we get into the details, let's first understand what we're talking about. Recently, the concept of a Metaverse has been popularized by Facebook's rebranding to Meta, but the story doesn't start there.

We're on the cusp of major breakthroughs in the metaverse, that collective world of new augmented reality (AR) and virtual reality (VR) experiences that is gaining steam. The new meta modality will not only be a huge new source of user and behavioral data for enterprises, but it will be a fertile ground for deploying rapidly maturing AI technologies like NLP and computer vision. AR and VR technologies have been simmering on the backburner for years. Up to this point, VR has primarily been used for immersive video games, although a few enterprise software companies have dabbled in AR and VR interfaces, including Looker before it was snapped up by Google Cloud. But Facebook CEO Mark Zuckerberg put the collective worlds of AR and VR, i.e. the metaverse, firmly on the front burner last month when he announced Facebook was changing its name to Meta.

"Our destiny is to become what we think, to have our thoughts become our bodies and our bodies become our thoughts." The next major technological platform for creative expansion of the mind will be cyberspace, or more specifically the Metaverse, a functional successor to today's 2D Internet, with virtual places instead of Webpages. The Internet and smartphones have enabled the rapid and cheap sharing of information, immersive computing will be able to provide the same for experiences. That means that just as we can read, listen to, and watch videos of anything we want today, soon we'll be able to experience stunning lifelike simulations in virtual reality indistinguishable from our physical world. We'll be walking and actively interacting in the Metaverse, not slavishly staring at the flat screens.

Apple has acquired Spaces, a VR company that offered both VR experiences and, after the pandemic hit, a way of bringing your virtual avatar into Zoom meetings. Protocol quotes an unnamed Apple spokesperson offering the usual boilerplate confirmation, saying that it had nothing else to add. Spaces is, or was, a company that started out offering free-roam VR experiences, similar to what The Void offered in those heady pre-pandemic times. It was spun out of DreamWorks, and its first project was a Terminator-themed VR game for up to four players. But the most interesting thing about it was the facial tracking it used to try and make its VR games more immersive than the competition.

For VR environments to be fully immersive, we must strive towards graphics that can mirror reality. Unfortunately, such high-level graphics are difficult to attain in real-time without running into problems with framerate and stuttering gameplay, which breaks immersion and can cause motion sickness in players. The consequence of this is that the majority of VR experiences must use simplistic graphics to keep the experience as smooth as possible. Fortunately, computer graphics titans Nvidia have been utilising deep learning techniques to make such dreams possible. "Deep Learning Super Sampling" is a technology developed by Nvidia which allows high-resolution images to be generated for a low-res image input, allowing for high-quality graphics for VR to be less costly than before.