Marc Erich Latoschik

This paper presents the first systematic investigation of how congruence in visual self-representation influences self-perception and behavior. We span a continuum from the physical self through avatars with graded self-similarity to clearly dissimilar avatars in virtual reality (VR). In a 1x4 within-user study, participants completed movement and quiz tasks in either physical reality or a digital twin environment in VR, where they embodied one of three avatars: a photorealistic self-similar avatar, a dissimilar same-gender avatar, or a dissimilar opposite-gender avatar. Subjective measures included presence, sense of embodiment, self-identification, and perceived change, and were complemented by an objective movement metric of behavioral change. Compared to physical reality, VR, even with a self-similar avatar, produced lower presence, a weaker sense of embodiment, and reduced self-identification, revealing a persistent gap in visual congruence. Within VR, self-similar avatars enhanced body ownership, self-location, and self-identification relative to dissimilar avatars. Conversely, dissimilar avatars produced measurable behavioral changes compared with self-similar ones. Gender cues, however, had little impact in gender-neutral tasks. Overall, the findings show that photorealistic self-similar avatars reinforce embodiment and self-identification. However, VR still falls short of achieving congruence with physical reality, underscoring key challenges for avatar realism and ecological validity.

Social Virtual Reality (SVR) allows users to meet and build relationships through embodied avatars and real-time interaction in virtual spaces. While embodiment can strengthen social connections and presence, it can also intensify negative encounters, making SVR particularly vulnerable to harassment. Despite frequent reports of verbal, visual, and “physical” violations in SVR, little is known about how harassment reshapes users' self-perception, including their sense of embodiment, self-identification, closeness, and avatar customization preferences. We conducted a controlled experiment with 52 participants who experienced either a neutral or a harassment condition in a scenario modeled after real SVR incidents. Participants perceived the harassing peer as significantly more negative, annoying, and disturbing than the neutral peer. Contrary to prior reports, harassment did not significantly affect well-being measures, including emotional state, self-esteem, and physiological arousal, within this controlled scenario. However, participants reported stronger bodily change, attributed more of their own attitudes and emotions to their avatars, and increased interpersonal distance when personal space was invaded. Self-reported coping strategies included ignoring, stepping back, using humor, and retaliating. Notably, avatar customization preferences shifted across conditions. Participants in the neutral condition favored personalized avatars, whereas those in the harassment condition more frequently preferred anonymity in public spaces. Together, these findings demonstrate that harassment in SVR not only exploits embodiment but also reshapes self-perception. We further contribute methodological insights into how harassment can be ethically and reproducibly studied in controlled SVR–like experiments.

Integrated Virtual Reality (IVR) systems are central to avatar-mediated use cases in Virtual Reality (VR), reconstructing users' movements on avatars. They differ primarily in their tracking architectures, which determine how completely and accurately users' movements are captured and reconstructed on avatars. Many current IVR systems reduce user-worn hardware, trading reconstruction accuracy against cost and setup complexity, yet their impact on user experience and task performance across use cases remains underexplored. We compared three reduced user-worn IVR systems. Each system has distinct technical approaches: (1) Captury (markerless outside-in optical tracking), (2) Meta Movement SDK (markerless inside-out optical tracking), and (3) Vive Trackers (marker-based outside-in optical tracking with IMUs). In a 3x5 mixed-design, participants performed five tasks, simulating different use cases, to probe distinct aspects of these systems. No system consistently outperformed the others. Meta excelled in hand-based, fast-paced interactions, while Captury and Vive performed better in lower-body tasks and during full-body pose observation. These findings underscore the need to evaluate reduced user-worn IVR systems within the specific use case. We offer practical guidance for system selection based on use-case demands and released our tasks as an open-source, extensible framework to support future evaluations for selecting IVR systems.

Immersive 3D mid-air sketching systems liberate users from the confines of traditional 2D sketching canvases. However, complications from perceptual challenges in Virtual Reality (VR), combined with the ergonomic and cognitive challenges of sketching in all three dimensions in mid-air lower the accuracy and aesthetic quality of 3D sketches. This paper explores how color-to-depth and opacity cues support users to create and perceive freehand, 3D strokes in room-scale sketching, unlocking a full 360°of freedom for creation. We implemented three graphic depth shader cues modifying the (1) alpha, (2) hue, and (3) value levels of a single color to dynamically adjust the color and transparency of meshes relative to their depth from the user. We investigated how these depth cues influence sketch efficiency, sketch quality, and total sketch experience with 24 participants in a comparative, counterbalanced, 4 × 1 within-subjects user study. First, with our graphic depth shader cues we could successfully transfer results of prior research in seated sketching tasks to room-scale scenarios. Our color-to-depth cues improved the similarity of sketches to target models. This highlights the usefulness of the color-to-depth approach even for the increased range of motion and depth in room-scale sketching. Second, our shaders assisted participants to complete tasks faster, spend a greater percentage of task time sketching, reduced the feeling of mental tiredness and improved the feeling of sketch efficiency in room-scale sketching. We discuss these findings and share our insights and conclusions to advance the research on improving spatial cognition in immersive sketching systems.

This study explores how people interact in dyadic social eXtended Reality (XR), focusing on two main factors: the animation type of a conversation partner's avatar and how immersed the user feels in the virtual environment. Specifically, we investigate how 1) idle behavior, 2) AI-generated gestures, and 3) motion-captured movements from a confederate (a controlled partner in the study) influence the quality of conversation and how that partner is perceived. We examined these effects in both symmetric interactions (where both participants use VR headsets and controllers) and asymmetric interactions (where one participant uses a desktop setup). We developed a social XR platform that supports asymmetric device configurations to provide varying levels of immersion. The platform also supports a modular avatar animation system providing idle behavior, real-time AI-generated co-speech gestures, and full-body motion capture. Using a 2×3 mixed design with 39 participants, we measured users' sense of spatial presence, their perception of the confederate, and the overall conversation quality. Our results show that users who were more immersed felt a stronger sense of presence and viewed their partner as more human-like and believable. Surprisingly, however, the type of avatar animation did not significantly affect conversation quality or how the partner was perceived. Participants often reported focusing more on what was said rather than how the avatar moved.

This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the performance and, in particular, the generalization capacity of the two models across applications. Our results indicate that while the models can accurately identify individuals within the same application, their ability to identify users across different XR applications remains limited. Overall, our results provide insight into current models generalization capabilities and suitability as biometric methods for user verification and identification. The results also serve as a much-needed risk assessment of hazardous and unwanted user identification in XR and Metaverse applications. Our cross-application XR motion dataset and code are made available to the public to encourage similar research on the generalization of motion-based user identification in typical Metaverse application use cases.