In this paper we study how the visual animation of a self-avatar can be artificially modified in real-time in order to generate different haptic perceptions. In our experimental setup, participants could watch their self-avatar in a virtual environment in mirror mode while performing a weight lifting task. Users could map their gestures on the self-animated avatar in real-time using a Kinect. We introduce three kinds of modification of the visual animation of the self-avatar according to the effort delivered by the virtual avatar: 1) changes on the spatial mapping between the user’s gestures and the avatar, 2) different motion profiles of the animation, and 3) changes in the posture of the avatar (upper-body inclination). The experimental task consisted of a weight lifting task in which participants had to order four virtual dumbbells according to their virtual weight. The user had to lift each virtual dumbbells by means of a tangible stick, the animation of the avatar was modulated according to the virtual weight of the dumbbell. The results showed that the altering the spatial mapping delivered the best performance. Nevertheless, participants globally appreciated all the different visual effects. Our results pave the way to the exploitation of such novel techniques in various VR applications such as sport training, exercise games, or industrial training scenarios in single or collaborative mode.

International audienceDatabases of spontaneous multimodal expressions of affective states occurring during a task are few. This paper presents a protocol for eliciting stress in a public speaking task. Behaviors of 19 participants were recorded via a multimodal setup including speech, video of the facial expressions and body movements, balance via a force plate, and physiological measures. Questionnaires were used to assert emotional states, personality profiles and relevant coping behaviors to study how participants cope with stressful situations. Several subjective and objective performances were also evaluated. Results show a significant impact of the overall task and conditions on the participants' emotional activation. The possible future use of this new multimodal emotional corpus is described

Postural interaction is of major importance during job interviews. While several prototypes enable users to rehearse for public speaking tasks and job interviews, few of these prototypes support subtle bodily interactions between the user and a virtual agent playing the role of an interviewer. The design of our system is informed

3D human motion capture by real-time monocular vision without using markers can be achieved by registering a 3D articulated model on a video. Registration consists in iteratively optimizing the match between primitives extracted from the model and the images with respect to the model position and joint angles. We extend a previous color-based registration algorithm with a more precise edge-based registration step. We present an experimental analysis of the residual error vs. the computation time and we discuss the balance between both approaches.