In the context of increasing urban risks, particularly from climate change-induced flooding, this paper presents an extended Reality (XR)-based framework to improve user risk training within urban built environments. The framework is designed to improve risk awareness and preparedness among various stakeholders, including citizens, local authorities, and emergency responders. Using immersive XR technologies, the training experience simulates real-world emergency scenarios, contributing to active participation and a deeper understanding of potential hazards and especially for floods. The framework highlights the importance of stakeholder participation in its development, ensuring that training modules are customized to address the specific needs of different user groups. The iterative approach of the framework supports ongoing refinement through user feedback and performance data, thus improving the overall effectiveness of risk training initiatives. This work outlines the methodological phases involved in the framework's implementation, including i) user flow mapping, ii) scenario selection, and iii) performance evaluation, with a focus on the pilot application in Senigallia, Italy. The findings underscore the potential of XR technologies to transform urban risk training, promoting a culture of preparedness and resilience against urban hazards.

AI-enhanced Extended Reality (XR) aims to deliver adaptive, immersive experiences--yet current systems fall short due to shallow user modeling and limited cognitive context. We introduce Perspective-Aware AI in Extended Reality (PAiR), a foundational framework for integrating Perspective-Aware AI (PAi) with XR to enable interpretable, context-aware experiences grounded in user identity. PAi is built on Chronicles--reasoning-ready identity models learned from multimodal digital footprints that capture users' cognitive and experiential evolution. PAiR employs these models in a closed-loop system linking dynamic user states with immersive environments. We present PAiR's architecture, detailing its modules and system flow, and demonstrate its utility through two proof-of-concept scenarios implemented in the Unity-based Open-Dome engine. PAiR opens a new direction for human-AI interaction by embedding perspective-based identity models into immersive systems.

Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR), is a transformative technology bridging the physical and virtual world and it has diverse potential which will be ubiquitous in the future. This review examines XR's evolution through foundational framework - hardware ranging from monitors to sensors and software ranging from visual tasks to user interface; highlights state of the art (SOTA) XR products with the comparison and analysis of performance based on their foundational framework; discusses how commercial XR devices can support the demand of high-quality performance focusing on spatial intelligence. For future directions, attention should be given to the integration of multi-modal AI and IoT-driven digital twins to enable adaptive XR systems. With the concept of spatial intelligence, future XR should establish a new digital space with realistic experience that benefits humanity. This review underscores the pivotal role of AI in unlocking XR as the next frontier in human-computer interaction.

This work explores how force feedback affects various aspects of robot data collection within the Extended Reality (XR) setting. Force feedback has been proved to enhance the user experience in Extended Reality (XR) by providing contact-rich information. However, its impact on robot data collection has not received much attention in the robotics community. This paper addresses this shortcoming by conducting an extensive user study on the effects of force feedback during data collection in XR. We extended two XR-based robot control interfaces, Kinesthetic Teaching and Motion Controllers, with haptic feedback features. The user study is conducted using manipulation tasks ranging from simple pick-place to complex peg assemble, requiring precise operations. The evaluations show that force feedback enhances task performance and user experience, particularly in tasks requiring high-precision manipulation. These improvements vary depending on the robot control interface and task complexity. This paper provides new insights into how different factors influence the impact of force feedback.

Extended Reality is a revolutionary method of delivering multimedia content to users. A large contributor to its popularity is the sense of immersion and interactivity enabled by having real-world motion reflected in the virtual experience accurately and immediately. This user motion, mainly caused by head rotations, induces several technical challenges. For instance, which content is generated and transmitted depends heavily on where the user is looking. Seamless systems, taking user motion into account proactively, will therefore require accurate predictions of upcoming rotations. Training and evaluating such predictors requires vast amounts of orientational input data, which is expensive to gather, as it requires human test subjects. A more feasible approach is to gather a modest dataset through test subjects, and then extend it to a more sizeable set using synthetic data generation methods. In this work, we present a head rotation time series generator based on TimeGAN, an extension of the well-known Generative Adversarial Network, designed specifically for generating time series. This approach is able to extend a dataset of head rotations with new samples closely matching the distribution of the measured time series.

The rapid advancement of Extended Reality (XR, encompassing AR, MR, and VR) and spatial computing technologies forms a foundational layer for the emerging Metaverse, enabling innovative applications across healthcare, education, manufacturing, and entertainment. However, research in this area is often limited by the lack of large, representative, and highquality application datasets that can support empirical studies and the development of new approaches benefiting XR software processes. In this paper, we introduce XRZoo, a comprehensive and curated dataset of XR applications designed to bridge this gap. XRZoo contains 12,528 free XR applications, spanning nine app stores, across all XR techniques (i.e., AR, MR, and VR) and use cases, with detailed metadata on key aspects such as application descriptions, application categories, release dates, user review numbers, and hardware specifications, etc. By making XRZoo publicly available, we aim to foster reproducible XR software engineering and security research, enable cross-disciplinary investigations, and also support the development of advanced XR systems by providing examples to developers. Our dataset serves as a valuable resource for researchers and practitioners interested in improving the scalability, usability, and effectiveness of XR applications. XRZoo will be released and actively maintained.

In the domain of Extended Reality (XR), particularly Virtual Reality (VR), extensive research has been devoted to harnessing this transformative technology in various real-world applications. However, a critical challenge that must be addressed before unleashing the full potential of XR in practical scenarios is to ensure robust security and safeguard user privacy. This paper presents a systematic survey of the utility of biometric characteristics applied in the XR environment. To this end, we present a comprehensive overview of the different types of biometric modalities used for authentication and representation of users in a virtual environment. We discuss different biometric vulnerability gateways in general XR systems for the first time in the literature along with taxonomy. A comprehensive discussion on generating and authenticating biometric-based photorealistic avatars in XR environments is presented with a stringent taxonomy. We also discuss the availability of different datasets that are widely employed in evaluating biometric authentication in XR environments together with performance evaluation metrics. Finally, we discuss the open challenges and potential future work that need to be addressed in the field of biometrics in XR.

With these advances, the way is paved for more efficient and safer lunar Lunar exploration has become a key focus, driving scientific and exploration, opening new possibilities for scientific research and technological advances. Ongoing missions are deploying rovers to technological developments in space. Thereby, to contribute to the the Moon's surface, targeting the far side and south pole. However, ever-advancing space sector, the presented work proposes a novel these terrains pose challenges, emphasizing the need for precise system to teleoperate rovers in unknown and hostile environments, obstacles and resource detection to avoid mission risks. This work able to detect relevant obstacles or resources on the lunar surface.

Recent developments in computer graphics, hardware, artificial intelligence (AI), and human-computer interaction likely lead to extended reality (XR) devices and setups being more pervasive. While these devices and setups provide users with interactive, engaging, and immersive experiences with different sensing modalities, such as eye and hand trackers, many non-player characters are utilized in a pre-scripted way or by conventional AI techniques. In this paper, we argue for using large language models (LLMs) in XR by embedding them in virtual avatars or as narratives to facilitate more inclusive experiences through prompt engineering according to user profiles and fine-tuning the LLMs for particular purposes. We argue that such inclusion will facilitate diversity for XR use. In addition, we believe that with the versatile conversational capabilities of LLMs, users will engage more with XR environments, which might help XR be more used in everyday life. Lastly, we speculate that combining the information provided to LLM-powered environments by the users and the biometric data obtained through the sensors might lead to novel privacy invasions. While studying such possible privacy invasions, user privacy concerns and preferences should also be investigated. In summary, despite some challenges, embedding LLMs into XR is a promising and novel research area with several opportunities.

OmniVoid is a software development company with a vision of filling the tech void. We offer a central family of OmniVoid products as well as engineer custom software solutions - tapping into the hidden potential of our client businesses and brands. Extended Reality (XR) and Artificial Intelligence (AI) are the technological pillars of our organization. Our staff comes from top-tier institutions which are at the forefront of tech. We call MIT, Harvard, Stanford, and several others home.