Aggression is a socially complex behaviour that intelligent virtual agents (IVAs) must convincingly convey in applications such as customer service and conflict training. Despite its importance, aggression remains understudied: prior work has focused on basic emotions and unimodal cues, providing little insight into how aggression can be modelled multimodally or systematically scaled by intensity. We present a psychologically grounded model that parametrises language, voice, body movement and facial expressions, across four aggression levels. We evaluated the model in two studies with 38 flight attendants. Experiment 1 tested unimodal cues, showing all modalities except language conveyed aggression gradients. Experiment 2 extended this by combining modalities, demonstrating that coordinated multimodal integration stabilised weaker language cues and produced perceptually robust aggression levels (low, mid, and high) with body and facial cues carrying most weight. Our work contributes the first validated multimodal, multi-level aggression model for IVAs, offering design principles for broader socially expressive agents.

Opportunities for medical students to gain practical experience in vaginal births are increasingly constrained by shortened clinical rotations, patient reluctance, and the unpredictable nature of labour. To alleviate clinicians' instructional burden and enhance trainees' learning efficiency, we introduce a mixed reality (MR) system for childbirth training that combines virtual guidance with tactile manikin interaction, thereby preserving authentic haptic feedback while enabling independent practice without continuous on-site expert supervision. The system extends the passthrough capability of commercial head-mounted displays (HMDs) by spatially calibrating an external RGB-D camera, allowing real-time visual integration of physical training objects. Building on this capability, we implement a coarse-to-fine localization pipeline that first aligns the maternal manikin with fiducial markers to define a delivery region and then registers the pre-scanned neonatal head within this area. This process enables spatially accurate overlay of virtual guiding hands near the manikin, allowing trainees to follow expert trajectories reinforced by haptic interaction. Experimental evaluations demonstrate that the system achieves accurate and stable manikin localization on a standalone headset, ensuring practical deployment without external computing resources. A large-scale user study involving 83 fourth-year medical students was subsequently conducted to compare MR-based and virtual reality (VR)-based childbirth training. Four senior obstetricians independently assessed performance using standardized criteria. Results showed that MR training achieved significantly higher scores in delivery, post-delivery, and overall task performance, and was consistently preferred by trainees over VR training. Although validated only in the context of obstetric delivery, the system demonstrates strong potential for broader manikin-based procedural training and other healthcare education scenarios.

Immersive virtual reality (VR) applications demand accurate, temporally coherent full-body pose tracking. Recent head-mounted camera-based approaches show promise in egocentric pose estimation, but encounter challenges when applied to VR head-mounted displays (HMDs), including temporal instability, inaccurate lower-body estimation, and the lack of real-time inference. To address these limitations, we present EgoPoseVR, an end-to-end framework for accurate egocentric full-body pose estimation in VR that integrates headset motion cues with egocentric RGB-D observations through a dual-modality fusion pipeline. A spatiotemporal encoder extracts frame- and joint-level representations, which are fused via cross-attention to fully exploit complementary motion cues across modalities. A kinematic optimization module then imposes constraints from HMD signals, enhancing the accuracy and stability of pose estimation. To facilitate training and evaluation, we introduce a large-scale synthetic dataset of over 1.8 million temporally aligned HMD and RGB-D frames across diverse VR scenarios. Experimental results show that EgoPoseVR outperforms state-of-the-art egocentric pose estimation models. A user study in real-world scenes further shows that EgoPoseVR achieved significantly higher subjective ratings in accuracy, stability, embodiment, and intention for future use compared to baseline methods. These results show that EgoPoseVR enables robust full-body pose tracking, offering a practical solution for accurate VR embodiment without requiring additional body-worn sensors or room-scale tracking systems.

The aviation industry faces increasing pilot training demands, and reliance on conventional Full Flight Simulators (FFS) limits training capacity. Virtual Reality (VR) offers scalable, remote training opportunities, but its role as a complement to FFS requires empirical validation. In collaboration with Singapore Airlines (SIA) instructor pilots, we developed a VR training prototype for a visual approach into Gimhae International Airport, emphasizing Competency-Based Training and Assessment (CBTA)-based Threat and Error Management (TEM). An empirical study with 39 SIA Boeing 737-MAX 8 type-rated first officers evaluated VR against FFS. An equivalence analysis showed that VR achieved performance outcomes comparable to FFS in 13 of the 16 Observable Behaviors (OBs) and across 4 Competencies. In addition, a comparative analysis indicated measurable performance improvements when VR was used to supplement FFS training. Our results suggest VR can meaningfully complement FFS in targeted competency areas, with future work required to assess its broader integration across additional scenarios and performance metrics.

State-of-the-art image matching methods have shown strong generalization across natural image datasets, but their effectiveness in complex surgical environments remains underexplored. Surgical scenes introduce unique challenges, including homogeneous tissue textures, variable lighting, and frequent occlusions, which can degrade the reliability of keypoint correspondences essential for downstream vision tasks such as camera pose estimation and structure-from-motion. In this study, we systematically evaluate leading image matching methods within laparoscopic surgical settings, emphasizing performance under resource-constrained conditions. We present an optimized evaluation pipeline that incorporates robust estimators to enhance correspondence filtering and assess their impact on pose estimation accuracy. Our approach also examines the influence of fine-tuning individual pipeline components, particularly robust estimators, on overall system performance. Mean Reprojection error is refined by thresholding the nearest ground truth projections, enabling a more precise characterization of matching accuracy. Across five robust estimators, FM_8PTS consistently demonstrates superior resilience to outliers. Our results establish RoMa as the leading model for balancing pose estimation accuracy, reprojection performance, and computational efficiency, making it suitable for real-time surgical applications. By providing the first systematic benchmark and actionable insights for optimizing image matching pipelines in surgical domains, this work sets a new standard and paves the way for more reliable, efficient, and clinically applicable image-guided tools in minimally invasive surgery.

Advancements in Virtual Reality (VR) technology have opened new frontiers in medical education, igniting interest among medical educators to incorporate it into mainstream curriculum, complementing traditional training modalities such as manikin training. Despite numerous VR simulators on the market, their uptake in medical education remains limited. This paper explores the acceptability and educational effectiveness of VR in the context of vaginal childbirth delivery training, with the simulator providing a walkthrough for the second and third stages of labour, contrasting it with established manikin-based methods. We conducted a large-scale empirical study with 117 medical students, revealing a significant 24.9% improvement in knowledge scores when using VR as compared to manikin. However, VR received significantly lower self-reported feasibility scores in Confidence, Usability, Enjoyment, Feedback and Presence, indicating low acceptance. The study provides critical insights into the relationship between technological innovation and educational impact, guiding future integration of VR into medical training curricula.