SMART VR is an intelligent training interface that combines immersive virtual reality with real-time biofeedback to support stress regulation in firefighter simulations. The system employs machine learning algorithms to interpret biometric data and adjust scenario parameters dynamically, creating a responsive and personalized training experience. Evaluation with professional trainees shows improved stress awareness and performance under pressure. This work contributes to the field of intelligent user interfaces by showcasing a novel application of physiological computing in adaptive training systems.
We present a learning-support system that integrates physical three-dimensional models with augmented reality (AR) to facilitate the acquisition of spatial-figure concepts. The system is implemented on an iPad, which functions as both input and output device. Following instructions displayed on the screen, users mentally construct the spatial figure that satisfies a displayed problem and then place vertex markers along the edges of a physical cube model. The iPad's rear camera tracks these markers, and the system superimposes the user-defined spatial figure onto the model in real-time AR, enabling the learner to examine the figure from multiple viewpoints. While interacting with the system, data-including responses, completion times, and pre- and post-task questionnaire scores-are recorded. These data are subsequently analyzed to evaluate learning outcomes and to inform iterative refinement of instructional content and exhibit design. An evaluation experiment was conducted at a university festival. Nineteen participants completed questionnaires assessing ease of use, perceived effectiveness for learning spatial figures, and interest. The results indicated generally positive evaluations. Although the system did not yield immediate, statistically significant learning gains-likely due to operational difficulty and the inherent challenge of the material-the integration of AR with tangible models appears promising for stimulating learner engagement in the comprehension of spatial figures.
Accurately predicting head motion is essential for reducing motion-to-photon (MTP) latency in XR systems. However, standard evaluation practices often conflate true prediction error with residual calibration artifacts-such as global rotation bias, lever-arm offsets, or sub-frame time skews-even after alignment. These small but realistic miscalibrations can dominate reported errors and obscure the actual performance of learningbased predictors, creating a gap in fair and robust benchmarking. This paper introduces a calibration-robust learning and evaluation pipeline for XR head-motion prediction that explicitly addresses these residuals. By rethinking both the training targets and evaluation protocol, our approach disentangles model accuracy from calibration artifacts, enabling more meaningful comparisons across devices and algorithms. Experiments on the public ADVIO dataset and our HMD-MoCap sessions show that body-frame targets consistently reduce calibration sensitivity for data-driven prediction models-yielding lower calibration sensitivity slopes (CSS) and higher robust area under the curve (AUC)-while maintaining short-horizon rotational accuracy compared to conventional world-frame approaches. Our findings establish a protocol for robust, reproducible benchmarking in XR pose prediction and demonstrate the feasibility of ground-truth-level head-motion prediction for EdgeXR.
As immersive technologies become more prevalent in entertainment, education and industry, the manual creation of 3D content remains a major bottleneck, demanding time, specialized labor and significant computational resources. This challenge is directly related to Sustainable Development Goal (SDG) 12, which calls for more sustainable consumption and production patterns, including more efficient use of materials, energy and human effort. In this work, we present an AI-based XR pipeline that automatically generates interactive VR scenes from a single RGB image, reducing the need for extensive manual 3D modeling. Our approach combines state-of-the-art 3D object detection, single-image mesh reconstruction and virtual reality visualization on consumer hardware (Meta Quest 3). Using images from the SUN RGB-D dataset and models such as Total3DUnderstanding, Implicit3DUnderstanding, Unique3D and Hunyuan3D-v2, we demonstrate how everyday environments can be rapidly digitized into VR-ready objects and scenes. Preliminary results indicate that recent reconstruction models substantially improve mesh quality and geometric fidelity, enabling reusable, editable assets that support sustainable XR applications, such as virtual product exploration, sustainable tourism experiences, and learning environments focused on consumption and waste. Our evaluation is qualitative and exploratory in nature, focusing on visual fidelity, reconstruction plausibility, and end-to-end feasibility rather than on numerical benchmarking. We discuss how this type of pipeline can contribute to SDG 12 by lowering the content-production barrier, encouraging the reuse of digital assets, and enabling end-users to transform their own spaces into immersive XR experiences without the need for large-scale production pipelines.
This paper presents a prototype of a mixed reality (MR) museum artifact preview system, enabling visitors to view virtual artifacts on real pedestals before entering the exhibit. The system uses MR devices to display 3D artifact previews with guide avatars and AI-generated introductory text, enhancing pre-visit engagement.
