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 1 ×4 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.

In data analysis, scatterplots serve as an initial tool for exploring the relationships between two or three attributes. While scatterplot matrices (SPLOMs) display every attribute combination through numerous 2D scatterplots to show a concise overview of a multivariate dataset, this approach is not directly suitable for 3D scatterplots due to visual clutter. Since research has shown that immersive virtual environments can enhance data analysis compared to traditional 2D desktop setups – especially for spatial analysis tasks – we propose an interactive system, called SPLOCIS, that makes use of virtual reality to enable users to interactively filter and select 3D scatter-plots from all possible attribute combinations. Our user study, combining both qualitative and quantitative results, demonstrates that SPLOCIS is a particularly novel and stimulating approach to work with multivariate data in immersive environments. It enables solving classic data exploration tasks in an efficient and accurate way, while not imposing unexpectedly high task loads. Moreover, our findings provide promising suggestions for further developments.

With the growing demand for realistic virtual humans, parametric body models have become a cornerstone of modern medicine, sports or entertainment applications. However, most of these models are inherently limited: they only capture the 3D surface of the skin, offering no insight into the complex bio-mechanical structures that generate motion. As more applications expand towards biomechanics, the need for virtual human models that go beyond the skin has become increasingly evident. Traditional soft-tissue simulations, such as FEM, are accurate but non-scalable and too computationally expensive for most common applications. Alternatively, existing biomechanical tools can simulate muscular forces and activations, but do not model changes in external shape, restricting how activations correlate with actual observable anatomy. This motivates a novel inverse research problem: recovering muscle deformations directly from visible surface observations - i.e., from the skin, and thus the pose. In this work, we present SOMA (from Surface Observations to Muscle Anatomy), a person-specific model that infers spatio-temporal muscle behavior from surface signals obtained using RGB cameras, and SKIM, a subject-specific soft-tissue deformation dataset. To the best of our knowledge, this is the first method that attempts to recover muscle deformations from multi-view RGB data. We show how our method provides anatomically grounded animations without the complexity of traditional simulations, leading to a scalable and cost-effective solution. Data and code are available.

This paper investigates if and how self-similarity and having motor control impact sense of embodiment, self-identification, and body weight perception in Augmented Reality (AR). We conducted a 2x2 mixed design experiment involving 60 participants who interacted with either synchronously moving virtual humans or independently moving ones, each with self-similar or generic appearances, across two consecutive AR sessions. Participants evaluated their sense of embodiment, self-identification, and body weight perception of the virtual human. Our results show that self-similarity significantly enhanced sense of embodiment, self-identification, and the accuracy of body weight estimates with the virtual human. However, the effects of having motor control over the virtual human movements were notably weaker in these measures than in similar VR studies. Further analysis indicated that not only the virtual human itself but also the participants' body weight, self-esteem, and body shape concerns predict body weight estimates across all conditions. Our work advances the understanding of virtual human body weight perception in AR systems, emphasizing the importance of factors such as coherence with the real-world environment.

A negative body image can have an impact on developing and maintaining obesity. Using virtual reality (VR) to conduct cognitive behavioral therapy (CBT) is an innovative approach to treat people with obesity. This multicenter non-randomized pilot study examined the feasibility and the effect on eating behavior and body perception of a newly developed VR system to conduct body image exercises. Participants with a body mass index (BMI) ≥ 30.0 kg/m2 without severe mental diseases attended three study visits in an interval of one to four weeks to receive virtual (VR intervention) or traditional (non-VR intervention) body image exercises. Data on anthropometrics, eating behavior (Dutch Eating Behavior Questionnaire, DEBQ), body perception (Body Shape Questionnaire, BSQ; Multidimensional Assessment of Interoceptive Awareness, MAIA), and satisfaction (standardized interview and questionnaire) were collected. In total, 66 participants (VR intervention: 31, non-VR intervention: 35) were included. The majority was female (52/66, 78.8

The presence of a conspecific can mitigate autonomic responses to aversive stimuli, an effect known as social buffering. Nowadays, social interactions are often virtual, yet virtual social buffering effects remain poorly understood. This work presents five studies that systematically test the conditions required for virtual social buffering. We assessed participants' emotion ratings and skin conductance responses when they were presented with neutral or fear-inducing sounds alone or in the presence of a virtual character with a varying extent of human-like features (virtual female or male person, wooden puppet, point cloud). The characters were presented using the same social framing, i.e., had the same social meaning. Our results show a significant reduction in SCR responses to fear-inducing sounds in the presence of a virtual character, but only if it is embodied as a woman or a wooden puppet. Clarifying the role of the social frame, a control study showed no social buffering effects if the wooden puppet was presented without the social frame. Our results show that the characteristics of a virtual character significantly moderate the social buffering of fear responses. Our findings shed light on the nature of virtual social buffering effects and are relevant for developing virtual applications for clinical and societal interventions.

Working with scatterplots is a classic everyday task for data analysts, which gets increasingly complex the more plots are required to form an understanding of the underlying data. To help analysts retrieve relevant plots more quickly when they are needed, immersive virtual environments (iVEs) provide them with the option to freely arrange scatterplots in the 3D space around them. In this paper, we investigate the impact of different virtual environments on the users' ability to quickly find and retrieve individual scatterplots from a larger collection. We tested three different scenarios, all having in common that users were able to position the plots freely in space according to their own needs, but each providing them with varying numbers of landmarks serving as visual cues: an Empty scene as a baseline condition, a single landmark condition with one prominent visual cue being a Desk, and a multiple landmarks condition being a virtual Office. Results from a between-subject investigation with 45 participants indicate that the time and effort users invest in arranging their plots within an iVE had a greater impact on memory performance than the design of the iVE itself. We report on the individual arrangement strategies that participants used to solve the task effectively and underline the importance of an active arrangement phase for supporting the spatial memorization of scatterplots in iVEs.

In co-located extended reality (XR) experiences, headsets occlude their wearers’ facial expressions, impeding natural conversation. We introduce two techniques to mitigate this using off-the-shelf hardware: compositing a view of a personalized avatar behind the visor (“see-through visor”) and reducing the headset’s visibility and showing the avatar’s head (“head substitution”). We evaluated them in a repeated-measures dyadic study (N = 25) that indicated promising effects. Collaboration with a confederate with our techniques, compared to a no-avatar baseline, resulted in quicker consensus in a judgment task and enhanced perceived mutual understanding. However, the avatar was also rated and commented on as uncanny, though participant comments indicate tolerance for avatar uncanniness since they restore gaze utility. Furthermore, performance in an executive task deteriorated in the presence of our techniques, indicating that our implementation drew participants’ attention to their partner’s avatar and away from the task. We suggest giving users agency over how these techniques are applied and recommend using the same representation across interaction partners to avoid power imbalances.

Body image distortions in anorexia nervosa pose significant therapeutic challenges, requiring innovative interventions. Virtual Reality (VR) and Augmented Reality (AR) technologies offer promising solutions, yet stakeholder preferences, from therapists and patients, remain unexplored. This methodological proposal outlines focus groups to compare VR and AR mirror exposures using personalized and body-weight-modifiable avatars in anorexia nervosa therapy. Therapists will evaluate therapeutic potential, risks, and practicality, while adolescent patients will assess comfort, stress responses, and usability. The findings aim to advance the user-centered integration of VR and AR into anorexia nervosa therapy, addressing critical treatment gaps.

Due to the increasing use of virtual avatars, the animation of head-hand interactions has recently gained attention. To this end, we present a novel volumetric and physics-based interaction simulation. In contrast to previous work, our simulation incorporates temporal effects such as collision paths, respects anatomical constraints, and can detect and simulate skin pulling. As a result, we can achieve more natural-looking interaction animations and take a step towards greater realism. However, like most complex and computationally expensive simulations, ours is not real-time capable even on high-end machines. Therefore, we train small and efficient neural networks as accurate approximations that achieve about 200 FPS on consumer GPUs, about 50 FPS on CPUs, and are learned in less than four hours for one person. In general, our focus is not to generalize the approximation networks to low-resolution head models but to adapt them to more detailed personalized avatars. Nevertheless, we show that these networks can learn to approximate our head-hand interaction model for multiple identities while maintaining computational efficiency. Since the quality of the simulations can only be judged subjectively, we conducted a comprehensive user study which confirms the improved realism of our approach. In addition, we provide extensive visual results and inspect the neural approximations quantitatively. All data used in this work has been recorded with a multi-view camera rig. Code and data are available at https://gitlab.cs.hs-rm.de/cvmr_releases/HeadHand.

Recent developments in smartphone-based avatar reconstruction have made the creation of personalized and realistic avatars significantly more accessible. However, relying on one smartphone camera leads to capturing images sequentially, which introduces new challenges; particularly longer capture times increase the susceptibility to subject motion, which results in degraded reconstructions. We present a novel approach for smartphone-based avatar reconstruction that combines photogrammetry, silhouette constraints, and inverse rendering to produce high-fidelity, realistic avatars free of motion-induced artifacts. By using short, motion-resilient image sequences, referred to as sub-scans, we considerably reduce motion-induced artifacts. Our pipeline achieves high visual quality while offering improved robustness and outperforms current state-of-the-art methods in terms of computation time and accuracy.

Body weight issues can manifest in low self-esteem through a negative body image or the feeling of unattractiveness. To explore potential interventions, the pilot study examined whether embodying a photorealistically personalized avatar with enhanced attractiveness affects self-esteem. Participants in the manipulation group adjusted their avatar’s body weight to their self-defined ideal, while a control group used unmodified avatars. To confirm the manipulation, we measured the perceived avatars’ attractiveness. Results showed that participants found avatars at their ideal weight significantly more attractive, confirming an effective manipulation. Further, the ideal weight group showed a clear trend towards higher self-esteem post-exposure.

Facial expressions are crucial for many eXtended Reality (XR) use cases, from mirrored self exposures to social XR, where users interact via their avatars as digital alter egos. However, current XR devices differ in sensor coverage of the face region. Hence, a faithful reconstruction of facial expressions either has to exclude these areas or synthesize missing animation data with model-based approaches, potentially leading to perceivable mismatches between executed and perceived expression. This paper investigates potential effects of the coverage of facial animations (none, partial, or whole) on important factors of self-perception. We exposed 83 participants to their mirrored personalized avatar. They were shown their mirrored avatar face with upper and lower face animation, upper face animation only, lower face animation only, or no face animation. Whole animations were rated higher in virtual embodiment and slightly lower in uncanniness. Missing animations did not differ from partial ones in terms of virtual embodiment. Contrasts showed significantly lower humanness, lower eeriness, and lower attractiveness for the partial conditions. For questions related to self-identification, effects were mixed. We discuss participants' shift in body part attention across conditions. Qualitative results show participants perceived their virtual representation as fascinating yet uncanny.

Realistic full-body avatars play a key role in representing users in virtual environments, where they have been shown to considerably improve important effects of immersive experiences such as body ownership and presence. Consequently, the demand for realistic virtual humans – and methods for creating them – is rapidly growing. However, despite extensive research into 3D reconstruction of avatars from real humans, an easy and affordable method for generating realistic and VR-capable avatars is still lacking: Existing methods are either limited to complex capture hardware and/or controlled lab environments, do not provide sufficient visual fidelity, or cannot be rendered at sufficient frame rates for multi-avatar VR applications. To make avatar reconstruction widely available, we developed Avatars for the Masses – a client-server-based online service for scanning real humans with an easy-to-use smartphone application that empowers even non-expert users to capture photorealistic and VR-ready avatars. The data captured by the smartphone is transferred to a reconstruction server, where the avatar is generated in a fully automated process. Our advancements in capturing and reconstructing allow for higher-quality avatars even in less controlled in-the-wild environments. Extensive qualitative and quantitative evaluations show our method’s avatars to be on par with the ones generated by expensive expert-operated systems. It also generates more accurate replicas in comparison to the current state of the art in smartphone-based reconstruction, produces much less artifacts and provides a much higher rendering performance in VR in comparison to three representative neural methods. A comprehensive user study confirms similar perception results compared to avatars reconstructed with expensive expert-operated systems, and it underscores a sufficient usability of the overall system. To truly bring avatars to the masses, we will make our smartphone application publicly available for research purposes. More details can be found on the project page: https://avatars.cs.tu-dortmund.de.

Our work investigates the influence of self-related cues in the design of virtual humans on body perception in virtual reality. In a $2\times 2$ mixed design, 64 participants faced photorealistic virtual humans either as a motion-synchronized embodied avatar or as an autonomous moving agent, appearing subsequently with a personalized and generic texture. Our results unveil that self-related cues through embodiment and personalization yield an individual and complemented increase in participants' sense of embodiment and self-identification towards the virtual human. Different body weight modification and estimation tasks further showed an impact of both factors on participants' body weight perception. Additional analyses revealed that the participant's body mass index predicted body weight estimations in all conditions and that participants' self-esteem and body shape concerns correlated with different body weight perception results. Hence, we have demonstrated the occurrence of double standards through induced self-related cues in virtual human perception, especially through embodiment.

We study the use of polyhedral discretizations for the solution of heat diffusion and elastodynamic problems in computer graphics. Polyhedral meshes are more natural for certain applications than pure triangular or quadrilateral meshes, which thus received significant interest as an alternative representation. We consider finite element methods using barycentric coordinates as basis functions and the modern virtual finite element approach. We evaluate them on a suite of classical graphics problems to understand their benefits and limitations compared to standard techniques on simplicial discretizations. Our analysis provides recommendations and a benchmark for developing polyhedral meshing techniques and corresponding analysis techniques.

Offline facial retargeting, i.e., transferring facial expressions from a source to a target character, is a common production task that still regularly leads to considerable algorithmic challenges. This task can be roughly dissected into the transfer of sequential facial animations and non-sequential blendshape personalization. Both problems are typically solved by data-driven methods that require an extensive corpus of costly target examples. Other than that, geometrically motivated approaches do not require intensive data collection but cannot account for character-specific deformations and are known to cause manifold visual artifacts.We present AnaConDaR, a novel method for offline facial retargeting, as a hybrid of data-driven and geometry-driven methods that incorporates anatomical constraints through a physics-based simulation. As a result, our approach combines the advantages of both paradigms while balancing out the respective disadvantages. In contrast to other recent concepts, AnaConDaR achieves substantially individualized results even when only a handful of target examples are available. At the same time, we do not make the common assumption that for each target example a matching source expression must be known. Instead, AnaConDaR establishes correspondences between the source and the target character by a data-driven embedding of the target examples in the source domain. We evaluate our offline facial retargeting algorithm visually, quantitatively, and in two user studies.

Discrete Laplacians are the basis for various tasks in geometry processing. While the most desirable properties of the discretization invariably lead to the so-called cotangent Laplacian for triangle meshes, applying the same principles to polygon Laplacians leaves degrees of freedom in their construction. From linear finite elements it is well-known how the shape of triangles affects both the error and the operator's condition. We notice that shape quality can be encapsulated as the trace of the Laplacian and suggest that trace minimization is a helpful tool to improve numerical behavior. We apply this observation to the polygon Laplacian constructed from a virtual triangulation [BHKB20] to derive optimal parameters per polygon. Moreover, we devise a smoothing approach for the vertices of a polygon mesh to minimize the trace. We analyze the properties of the optimized discrete operators and show their superiority over generic parameter selection in theory and through various experiments.

Virtual humans play a pivotal role in social virtual environments, shaping users’ VR experiences. The diversity in available options and users’ individual preferences can result in a heterogeneous mix of appearances among a group of virtual humans. The resulting variety in higher-order anthropomorphic and realistic cues introduces multiple (in)congruencies, eventually impacting the plausibility of the experience. However, related work investigating the effects of being co-located with multiple virtual humans of different appearances remains limited. In this work, we consider the impact of (in)congruencies in the realism of a group of virtual humans, including co-located others (agents) and one’s self-representation (self-avatar), on users’ individual VR experiences. In a 2×3 mixed design, participants embodied either (1) a personalized realistic or (2) a customized stylized self-avatar across three consecutive VR exposures in which they were accompanied by a group of virtual others being either (1) all realistic, (2) all stylized, or (3) mixed between stylized and realistic. Our results indicate groups of virtual others of higher realism, i.e., potentially more congruent with participants’ real-world experiences and expectations, were considered more human-like, increasing the feeling of co-presence and the impression of interaction possibilities. (In)congruencies concerning the homogeneity of the group did not cause considerable effects. Furthermore, our results indicate that a self-avatar’s congruence with the participant’s real-world experiences concerning their own physical body yielded notable benefits for virtual body ownership and self-identification for realistic personalized avatars. Notably, the incongruence between a stylized self-avatar and a group of realistic virtual others resulted in diminished ratings of self-location and self-identification. This suggests that higher-order (in)congruent visual cues that are not within the ego-central referential frame of one’s (virtual) body, can have an (adverse) effect on the relationship between one’s self and body. We conclude on the implications of our findings and discuss our results within current theories of VR experiences, considering (in)congruent visual cues and their impact on the perception of virtual others, self-representation, and spatial presence.