The perception of one's own body is subject to systematic distortions and can be influenced by exposure to visual stimuli showing distorted bodies. In Virtual Reality (VR), echoing such body judgment inaccuracies, avatars with strong appearance dissimilarities with respect to users' bodies can be successfully embodied. The present experimental work investigates, in the healthy population, the perception of the own body in immersive and embodied VR, as well as the impact of being co-present with virtual humans on such self-perception. Participants were successively presented with different avatars, corresponding to various upper- and lower-body proportions, and were asked to compare them with their perceived own body morphology. To investigate the influence of co-present virtual humans on this judgment, the task was performed in co-presence with virtual agents corresponding to various body appearances. Results show an overall overestimation of one's leg length and no influence of the co-present agent's appearance. Importantly, the embodiment scores reflect such body morphology judgment inaccuracy, with participants reporting lower levels of embodiment for avatars with very short legs than for avatars with very long legs. Our findings suggest specifics of embodied body judgment methods, likely resulting from the experience of embodying the avatar as compared to visual appreciation only.

The sense of embodiment in virtual reality (VR) is commonly understood as the subjective experience that one's physical body is substituted by a virtual counterpart, and is typically achieved when the avatar's body, seen from a first-person view, moves like one's physical body. Embodiment can also be experienced in other circumstances (e.g., in third-person view) or with imprecise or distorted visuo-motor coupling. It was moreover observed, in various cases of small or progressive temporal and spatial manipulations of avatars' movements, that participants may spontaneously follow the movement shown by the avatar. The present work investigates whether, in some specific contexts, participants would follow what their avatar does even when large movement discrepancies occur, thereby extending the scope of understanding of the self-avatar follower effect beyond subtle changes of motion or speed manipulations. We conducted an experimental study in which we introduced uncertainty about which movement to perform at specific times and analyzed participants' movements and subjective feedback after their avatar showed them an incorrect movement. Results show that, when in doubt, participants were influenced by their avatar's movements, leading them to perform that particular error twice more often than normal. Importantly, results of the embodiment score indicate that participants experienced a dissociation with their avatar at those times. Overall, these observations not only demonstrate the possibility of provoking situations in which participants follow the guidance of their avatar for large motor distortions, despite their awareness about the avatar movement disruption and on the possible influence it had on their choice, and, importantly, exemplify how the cognitive mechanism of embodiment is deeply rooted in the necessity of having a body.

In Virtual Reality, having a virtual body opens a wide range of possibilities as the participant's avatar can appear to be quite different from oneself for the sake of the targeted application (e.g., for perspective-taking). In addition, the system can partially manipulate the displayed avatar movement through some distortion to make the overall experience more enjoyable and effective (e.g., training, exercising, rehabilitation). Despite its potential, an excessive distortion may become noticeable and break the feeling of being embodied into the avatar. Past researches have shown that individuals have a relatively high tolerance to movement distortions and a great variability of individual sensitivities to distortions. In this article, we propose a method taking advantage of Reinforcement Learning (RL) to efficiently identify the magnitude of the maximum distortion that does not get noticed by an individual (further noted the detection threshold). We show through a controlled experiment with subjects that the RL method finds a more robust detection threshold compared to the adaptive staircase method, i.e., it is more able to prevent subjects from detecting the distortion when its amplitude is equal or below the threshold. Finally, the associated majority voting system makes the RL method able to handle more noise within the forced choices input than adaptive staircase. This last feature is essential for future use with physiological signals as these latter are even more susceptible to noise. It would then allow to calibrate embodiment individually to increase the effectiveness of the proposed interactions.

When placed inside an immersive virtual simulation, subjects will tend to experience the feeling of being ’really there’ and to respond realistically to their environment, forgetting that it is not real. This behaviour is observed when subjects experience a high sense of presence, the sensation of being in a real place and that the scenario being depicted to them is real. Here we present an experiment designed to evaluate the impact of different levels of immersion, and of different blending of virtual and real objects and body representations, on participant’s subjective experience. Presence is evaluated with an innovative method combining the random introduction of breaks-in-presence (BiP) with a rapid decision-making test. Results show that the level of immersion impacts both the Sense of Presence (SoP) and the Sense of Embodiment (SoE), that the BiP has a limited impact on the SoE without breaking it, and that the level of confidence in the decision test correlates with both the SoP and the SoE.

Functional magnetic resonance Brain Imaging (fMRI) is a key non-invasive imaging technique for the study of human brain activity. Its millimetric spatial resolution is at the cost of several constraints: participants must remain static and experience artificial stimuli, making it difficult to generalize neuroscientific results to naturalistic and ecological conditions. Immersive Virtual Reality (VR) provides alternatives to such stimuli through simulation, but still requires an active first-person exploration of the environment to evoke a strong sense of presence in the virtual environment. Here, we report how to compensate for the inability to freely move in VR by leveraging on principles of embodiment for a virtual avatar, to eventually evoke a strong sense of presence with a minimal motion of the participant. We validated the functionality of the platform in a study where healthy participants performed several basic research tasks in an MR-specific immersive virtual environment. Our results show that our approach can lead to high sense of presence, strong body ownership, and sense of agency for a virtual avatar, with low movement-related MRI artifacts. Moreover, to exemplify the versatility of the platform, we reproduced several behavioral and fMRI results in the perceptual, motor, and cognitive domains. We discuss how to leverage such technology for neuroscience research and provide recommendations on efficient ways to implement and develop it successfully.

This study attempts to evaluate whether a navigation technique based on scaling the user’s avatar impacts the user’s ability to correctly assess the size of virtual objects in a virtual environment. This study was realized during the CERN Open Days with data from 177 participants over eighteen years old. We were able to observe well-established phenomena such as the effect of inter-pupillary distance (IPD) on perception of scale, as well as original results associated with scaling factor and avatar embodiment. We observed that the user is more prone to overestimate object sizes from the Virtual Environment (VE) when provided with an avatar, while scaling the IPD according to the scale of the user’s avatar contributes to a reduction in the overestimation of object sizes within the VE.

Performing motor tasks in virtual environments is best achieved with motion capture and animation of a 3D character that participants control in real time and perceive as being their avatar in the virtual environment. A strong Sense of Embodiment (SoE) for the virtual body not only relies on the feeling that the virtual body is their own (body ownership), but also that the virtual body moves in the world according to their will and replicates precisely their body movement (sense of agency). Within that frame of mind our specific aim is to demonstrate that the avatar can even be programmed to be better at executing a given task or to perform a movement that is normally difficult or impossible to execute precisely by the user. More specifically, our experimental task consists in asking subjects to follow with the hand a target that is animated using non-biological motion; the unnatural nature of the movement leads to systematic errors by the subjects. The challenge here is to introduce a subtle distortion between the position of the real hand and the position of the virtual hand, so that the virtual hand succeeds in performing the task while still letting subjects believe they are fully in control. Results of two experiments (N=16) show that our implementation of a distortion function, that we name the attraction well, successfully led participants to report being in control of the movement (agency) and being embodied in the avatar (body ownership) even when the distortion was above a threshold that they can detect. Furthermore, a progressive introduction of the distortion (starting without help, and introducing distortion on the go) could even further increase its acceptance.

Hands deserve particular attention in virtual reality (VR) applications because they represent our primary means for interacting with the environment. Although marker-based motion capture with inverse kinematics works adequately for full body tracking, it is less reliable for small body parts such as hands and fingers which are often occluded when captured optically, thus leading VR professionals to rely on additional systems (e.g. inertial trackers). We present a machine learning pipeline to track hands and fingers using solely a motion capture system based on cameras and active markers. Our finger animation is performed by a predictive model based on neural networks trained on a movements dataset acquired from several subjects with a complementary capture system. We employ a two-stage pipeline, which first resolves occlusions, and then recovers all joint transformations. We show that our method compares favorably to inverse kinematics by inferring automatically the constraints from the data, provides a natural reconstruction of postures, and handles occlusions better than three proposed baselines.

We investigate the self-attribution of distorted pointing movements in immersive virtual reality. Participants had to complete a multidirectional pointing task in which the visual feedback of the tapping finger could be deviated in order to increase or decrease the motor size of a target relative to its visual appearance. This manipulation effectively makes the task easier or harder than the visual feedback suggests. Participants were asked whether the seen movement was equivalent to the movement they performed, and whether they have been successful in the task. We show that participants are often unaware of the movement manipulation, even when it requires higher pointing precision than suggested by the visual feedback. Moreover, subjects tend to self-attribute movements that have been modified to make the task easier more often than movements that have not been distorted. We discuss the implications and applications of our results.

Hands in virtual reality applications represent our primary means for interacting with the environment. Although marker-based motion capture with inverse kinematics (IK) works for body tracking, it is less reliable for fingers often occluded when captured with cameras. Many computer vision and virtual reality applications circumvent the problem by using an additional system (e.g. inertial trackers). We explore an alternative solution that tracks hands and fingers using solely a motion capture system based on cameras and active markers with machine learning techniques. Our animation of fingers is performed by a predictive model based on neural networks, which is trained on a movements dataset acquired from several subjects with a complementary capture system (inertial). The system is as efficient as a traditional IK algorithm, provides a natural reconstruction of postures, and handles occlusions.

This study explores the extent to which individuals embodied in Virtual Reality tend to self-attribute the movements of their avatar. More specifically, we tested subjects performing goal-directed movements and distorted the mapping between user and avatar movements by decreasing or increasing the amplitude of the avatar hand movement required to reach for a target, while maintaining the apparent amplitude – visual distance – fixed. In two experiments, we asked subjects to report whether the movement that they have seen matched the movement that they have performed, or asked them to classify whether a distortion was making the task easier or harder to complete. Our results show that subjects perform poorly in detecting discrepancies when the nature of the distortion is not made explicit and that subjects are biased to self-attributing distorted movements that make the task easier. These findings, in line with previous accounts on the sense of agency, demonstrate the flexibility of avatar embodiment and open new perspectives for the design of guided interactions in Virtual Reality.

With the broad range of motion capture devices available on the market, it is now commonplace to directly control the limb movement of an avatar during immersion in a virtual environment. Here, we study how the subjective experience of embodying a full-body controlled avatar is influenced by motor alteration and self-contact mismatches. Self-contact is in particular a strong source of passive haptic feedback and we assume it to bring a clear benefit in terms of embodiment. For evaluating this hypothesis, we experimentally manipulate self-contacts and the virtual hand displacement relatively to the body. We introduce these body posture transformations to experimentally reproduce the imperfect or incorrect mapping between real and virtual bodies, with the goal of quantifying the limits of acceptance for distorted mapping on the reported body ownership and agency. We first describe how we exploit egocentric coordinate representations to perform a motion capture ensuring that real and virtual hands coincide whenever the real hand is in contact with the body. Then, we present a pilot study that focuses on quantifying our sensitivity to visuo-tactile mismatches. The results are then used to design our main study with two factors, offset (for self-contact) and amplitude (for movement amplification). Our main result shows that subjects' embodiment remains important, even when an artificially amplified movement of the hand was performed, but provided that correct self-contacts are ensured.

This project investigates the impact of a virtual zero gravity experience on the human gravity model. In the planned experiment, subjects are immersed with HMD and full body motion capture in a virtual world exhibiting either normal gravity or the apparent absence of gravity (i.e. body and objects floating in space). The study evaluates changes in the subjects' gravity model by observing changes on motor planning of actions dependent on gravity. Our goal is to demonstrate that a virtual reality exposure can induce some modifications to the humans internal gravity model, analogous to those resulting from real exposure (e.g. parabolic flights), even if users remain under normal gravity condition in reality.

In this paper we evaluate the use of non-planar projections as a means to increase the Field of View (FoV) in embodied Virtual Reality (VR). Our main goal is to bring the virtual body into the user's FoV and to understand how this affects the virtual body/environment relation and quality of interaction. Subjects wore a Head Mounted Display (HMD) and were instructed to perform a selection and docking task while using either Perspective (≈ 106 ° vertical FoV), Hammer or Equirectangular (≈ 180 ° vertical FoV for both) projection. The increased FoV allowed for a shorter search time as well as less head movements. However, quality of interaction was generally inferior, requiring more time to dock, increasing docking error and producing more body/environment collisions. We also assessed cybersickness and the sense of embodiment toward the virtual body through questionnaires, for which the difference between projections seemed to be less pronounced.