Volumetric medical imaging offers great potential for understanding complex pathologies. Yet, traditional 2D slices provide little support for interpreting spatial relationships, forcing users to mentally reconstruct anatomy into three dimensions. Direct volumetric path tracing and VR rendering can improve perception but are computationally expensive, while precomputed representations, like Gaussian Splatting, require planning ahead. Both approaches limit interactive use.We propose a hybrid rendering approach for high-quality, interactive, and immersive anatomical visualization. Our method combines streamed foveated path tracing with a lightweight Gaussian Splatting approximation of the periphery. The peripheral model generation is optimized with volume data and continuously refined using foveal renderings, enabling interactive updates. Depth-guided reprojection further improves robustness to latency and allows users to balance fidelity with refresh rate. We compare our method against direct path tracing and Gaussian Splatting. Our results highlight how their combination can preserve strengths in visual quality while re-generating the peripheral model in under a second, eliminating extensive preprocessing and approximations. This opens new options for interactive medical visualization.

Immersive virtual reality learning environments (IVRLEs) are increasingly used in medical education, yet the role of environmental fidelity-particularly scene design-remains underexplored. This study examines how varying levels of fidelity and contextualization affect motivational and cognitive outcomes. Eighty-seven medical students were randomly assigned to one of three scene conditions: a minimalistic "Blank Scene," a "Reconstructed Classroom Scene", or an "Inside-Human Scene". All students used a custom-developed application to learn about embryonic heart development. We measured virtual presence, intrinsic motivation, cognitive load, learning outcomes, and usability. Results showed that scene design influenced virtual presence, selected aspects of intrinsic motivation, cognitive load, and learning outcomes. The Inside-Human Scene elicited higher physical and self-presence as well as higher comprehension scores compared to the Reconstructed Classroom Scene. The Reconstructed Classroom Scene was associated with higher extraneous cognitive load. Intrinsic cognitive load was rated higher in the Inside-Human Scene, while germane cognitive load did not differ between conditions. No significant differences were found for task performance or factual recall. Overall, the findings indicate that scene design in IVRLEs affects how learners engage with complex content and may support deeper understanding when perceptual and contextual properties are coherent, while visually detailed environments may increase extraneous cognitive demands without improving learning.

In medical image visualization, path tracing of volumetric medical data like computed tomography (CT) scans produces lifelike three-dimensional visualizations. Immersive virtual reality (VR) displays can further enhance the understanding of complex anatomies. Going beyond the diagnostic quality of traditional 2D slices, they enable interactive 3D evaluation of anatomies, supporting medical education and planning. Rendering high-quality visualizations in real-time, however, is computationally intensive and impractical for compute-constrained devices like mobile headsets. We propose a novel approach utilizing Gaussian Splatting (GS) to create an efficient but static intermediate representation of CT scans. We introduce a layered GS representation, incrementally including different anatomical structures while minimizing overlap and extending the GS training to remove inactive Gaussians. We further compress the created model with clustering across layers. Our approach achieves interactive frame rates while preserving anatomical structures, with quality adjustable to the target hardware. Compared to standard GS, our representation retains some of the explorative qualities initially enabled by immersive path tracing. Selective activation and clipping of layers are possible at rendering time, adding a degree of interactivity to otherwise static GS models. This could enable scenarios where high computational demands would otherwise prohibit using path-traced medical volumes.

Creating a compelling sense of presence and embodiment can enhance the user experience in virtual reality (VR). One method to accomplish this is through self-representation with embodied personalized avatars or video self-avatars. However, these approaches require external hardware and primarily evaluate hand representations in VR across various tasks. We therefore present in this paper an alternative approach: video Pass-Through Embodiment (PTE), which utilizes the per-eye real-time depth map from Head-Mounted Displays (HMDs) traditionally used for Augmented Reality features. This method allows the user's real body to be cut out of the pass-through video stream and be represented in the VR environment without the need for additional hardware. To evaluate our approach, we conducted a between-subjects study involving 40 participants who completed a seated object sorting task using either PTE or a customized avatar. The results show that PTE, despite its limited depth resolution that leads to some visual artifacts, significantly enhances the user's sense of presence and embodiment. In addition, PTE does not negatively affect task performance, cognitive load, or cause VR sickness. These findings imply that video pass-through embodiment offers a practical and efficient alternative to traditional avatar-based methods in VR.

Low motion-to-photon latencies between physical movement and rendering updates are crucial for an immersive virtual reality (VR) experience and to avoidusers’ discomfort and sickness. Current methods aim to minimize the delay between the motion measurement and rendering at the cost of increasing technical complexity and possibly decreasing accuracy. By relying on capturing physical motion, these strategies will, by nature, not result in zero latency rendering or will be based on prediction and resulting uncertainty. This paper presents and evaluates a novel alternative and proof of principle for VR motion tracking that could enable motion-to-photon latencies of zero and below zero in time. We termed our concept Neural Motion Tracking, which we define as the sensing and assessment of motion through human neural activation of the somatic nervous system. In contrast to measuring physical activity, the key principle is that we aim to utilize the physiological timeframe between a user’s intention and the execution of motion. We aim to foresee upcoming motion ahead of the physical movement, by sampling preceding electromyographic signals before the muscle activation. The electromechanical delay (EMD) between potential change in the muscle activation and actual physical movement opens a gap in which measurement can be taken and evaluated before the physical motion. In a first proof of principle, we evaluated the concept with two activities, arm bending and head rotation, measured with a binary activation measure. Our results indicate that it is possible to predict movement and update a rendering up to 2 ms before its physical execution, which is assessed by optical tracking after approximately 4 ms. However, to make the best use of this advantage, electromyography (EMG) sensor data should be as high quality as possible (i.e., low noise and from muscle-near electrodes). Our results empirically quantify this characteristic for the first time when compared to state-of-the-art optical tracking systems for VR. We discuss our results and potential pathways to motivate further work toward marker- and latency-less motion tracking.

Deep learning-based object recognition, 6D pose estimation, and semantic scene understanding require a large amount of training data to achieve generalization. Time-consuming annotation processes, privacy, and security aspects lead to a scarcity of real-world datasets. To overcome this lack of data, synthetic data generation has been proposed, including multiple facets in the area of domain randomization to extend the data distribution. The objective of this review is to identify methods applied for synthetic data generation aiming to improve 6D pose estimation, object recognition, and semantic scene understanding in indoor scenarios. We further review methods used to extend the data distribution and discuss best practices to bridge the gap between synthetic and real-world data. We adhered to the guidelines of the systematic PRISMA technique. Three databases, IEEE Xplore, Springer Link, and ACM, and an additional manual search were conducted. In total, we identified 241 studies and included 34 in our systematic review. In summary, synthetic data generation has been performed using crop-out methods, graphic APIs, 3D modeling or authoring tools, or game engine-based methods. To extend the data distribution, varying scene parameters, i.e., lighting conditions or textures and the use of distracting objects in the scene are promising.

Human cognition relies on embodiment as a fundamental mechanism. Virtual avatars allow users to experience the adaptation, control, and perceptual illusion of alternative bodies. Although virtual bodies have medical applications in motor rehabilitation and therapeutic interventions, their potential for learning anatomy and medical communication remains underexplored. For learners and patients, anatomy, procedures, and medical imaging can be abstract and difficult to grasp. Experiencing anatomies, injuries, and treatments virtually through one's own body could be a valuable tool for fostering understanding. This work investigates the impact of avatars displaying anatomy and injuries suitable for such medical simulations. We ran a user study utilizing a skeleton avatar and virtual injuries, comparing to a healthy human avatar as a baseline. We evaluate the influence on embodiment, well-being, and presence with self-report questionnaires, as well as motor performance via an arm movement task. Our results show that while both anatomical representation and injuries increase feelings of eeriness, there are no negative effects on embodiment, well-being, presence, or motor performance. These findings suggest that virtual representations of anatomy and injuries are suitable for medical visualizations targeting learning or communication without significantly affecting users' mental state or physical control within the simulation.

This paper presents a novel approach for the augmentation of social behaviors in virtual reality (VR). We designed three visual transformations for behavioral phenomena crucial to everyday social interactions: eye contact, joint attention, and grouping. To evaluate the approach, we let users interact socially in a virtual museum using a large-scale multi-user tracking environment. Using a between-subject design (N = 125) we formed groups of five participants. Participants were represented as simplified avatars and experienced the virtual museum simultaneously, either with or without the augmentations. Our results indicate that our approach can significantly increase social presence in multi-user environments and that the augmented experience appears more thought-provoking. Furthermore, the augmentations seem also to affect the actual behavior of participants with regard to more eye contact and more focus on avatars/objects in the scene. We interpret these findings as first indicators for the potential of social augmentations to impact social perception and behavior in VR.