The present study investigates users' movement behavior in a virtual environment when they attempted to avoid a virtual character. At each iteration of the experiment, four conditions (Self-Avatar LookAt, No Self-Avatar LookAt, Self-Avatar No LookAt, and No Self-Avatar No LookAt) were applied to examine users' movement behavior based on kinematic measures. During the experiment, 52 participants were asked to walk from a starting position to a target position. A virtual character was placed at the midpoint. Participants were asked to wear a head-mounted display throughout the task, and their locomotion was captured using a motion capture suit. We analyzed the captured trajectories of the participants' routes on four kinematic measures to explore whether the four experimental conditions influenced the paths they took. The results indicated that the Self-Avatar LookAt condition affected the path the participants chose more significantly than the other three conditions in terms of length, duration, and deviation, but not in terms of speed. Overall, the length and duration of the task, as well as the deviation of the trajectory from the straight line, were greater when a self-avatar represented participants. An additional effect on kinematic measures was found in the LookAt (Gaze) conditions. Implications for future research are discussed.
The effects of gaze interaction between users and virtual characters has also been examined in the past. It has been found that during interactions with humans, the gaze @cite_17 and mutual eye contact @cite_36 can be interpreted as a core social interaction mechanism and main social interaction factor.
@cite_36 @cite_17
2889428601 1988798713 2038770833 2112800929
This paper presents a study performed in virtual reality on the effect of gaze interception during collision avoidance between two walkers. In such a situation, mutual gaze can be considered as a form of nonverbal communication. Additionally, gaze is believed to detail future path intentions and to be part of the nonverbal negotiation to achieve avoidance collaboratively. We considered an avoidance task between a real subject and a virtual human character and studied the influence of the character's gaze direction on the avoidance behaviour of the participant. Virtual reality provided an accurate control of the situation: seventeen participants were immersed in a virtual environment, instructed to navigate across a virtual space using a joystick and to avoid a virtual character that would appear from either side. The character would either gaze or not towards the participant. Further, the character would either perform or not a reciprocal adaptation of its trajectory to avoid a potential collision with the participant. The findings of this paper were that during an orthogonal collision avoidance task, gaze behaviour did not influence the collision avoidance behaviour of the participants. Further, the addition of reciprocal collision avoidance with gaze did not modify the collision behaviour of participants. These results suggest that for the duration of interaction in such a task, body motion cues were sufficient for coordination and regulation. We discuss the possible exploitation of these results to improve the design of virtual characters for populated virtual environments and user interaction. Eye contact plays a crucial role in our everyday social interactions. The ability of a device to reliably detect when a person is looking at it can lead to powerful human-object interfaces. Today, most gaze-based interactive systems rely on gaze tracking technology. Unfortunately, current gaze tracking techniques require active infrared illumination, calibration, or are sensitive to distance and pose. In this work, we propose a different solution-a passive, appearance-based approach for sensing eye contact in an image. By focusing on gaze *locking* rather than gaze tracking, we exploit the special appearance of direct eye gaze, achieving a Matthews correlation coefficient (MCC) of over 0.83 at long distances (up to 18 m) and large pose variations (up to ±30° of head yaw rotation) using a very basic classifier and without calibration. To train our detector, we also created a large publicly available gaze data set: 5,880 images of 56 people over varying gaze directions and head poses. We demonstrate how our method facilitates human-object interaction, user analytics, image filtering, and gaze-triggered photography. This study shows that humans (a) infer other people's movement trajectories from their gaze direction and (b) use this information to guide their own visual scanning of the environment and plan their own movement. In two eye-tracking experiments, participants viewed an animated character walking directly toward them on a street. The character looked constantly to the left or to the right (Experiment 1) or suddenly shifted his gaze from direct to the left or to the right (Experiment 2). Participants had to decide on which side they would skirt the character. They shifted their gaze toward the direction in which the character was not gazing, that is, away from his gaze, and chose to skirt him on that side. Gaze following is not always an obligatory social reflex; social-cognitive evaluations of gaze direction can lead to reversed gaze-following behavior. Abstract Tracking eye-movements provides easy access to cognitive processes involved in visual and sensorimotor processing. More recently, the underlying neural mechanisms have been examined by combining eye-tracking and functional neuroimaging methods. Apart from extracting visual information, gaze also serves important functions in social interactions. As a deictic cue, gaze can be used to direct the attention of another person to an object. Conversely, by following other persons’ gaze we gain access to their attentional focus, which is essential for understanding their mental states. Social gaze has therefore been studied extensively to understand the social brain. In this endeavor, gaze has mostly been investigated from an observational perspective using static displays of faces and eyes. However, there is growing consent that observational paradigms are insufficient for an understanding of the neural mechanisms of social gaze behavior, which typically involve active engagement in social interactions. Recent methodological advances have allowed increasing ecological validity by studying gaze in face-to-face encounters in real-time. Such improvements include interactions using virtual agents in gaze-contingent eye-tracking paradigms, live interactions via video feeds, and dual eye-tracking in two-person setups. These novel approaches can be used to analyze brain activity related to social gaze behavior. This review introduces these methodologies and discusses recent findings on the behavioral functions and neural mechanisms of gaze processing in social interaction.