With the growing presence of AI-driven avatars in everyday applications, understanding how users perceive and respond to such agents becomes increasingly important. A key open question is whether gaze awareness, an avatar’s ability to react to the gaze of a user, contributes to more natural and socially engaging interaction, particularly on conventional 2D screens. We present a real-time system that integrates live speech recognition, LLM-generated responses, and gaze feedback to enable naturalistic unscripted conversations with a virtual avatar. We conducted a within-subjects study (N = 17) comparing a gaze-aware avatar with a nonreactive baseline. The results of the eye-tracking show that gaze awareness significantly altered user eye movement patterns, increasing mutual gaze episodes, and generating subconscious gaze regulation. Our findings demonstrate that realistic gaze behavior affects user interaction on a subconscious level, even outside immersive environments. We discuss implications for designing future conversational AI systems that foster natural social presence.

Gaze-aware interfaces should work on all display sizes. This paper researches whether angular velocity or tangential speed should be kept when scaling a gaze-aware interface based on circular smooth pursuits to another display size. We also address the question of which target speed and which trajectory size feels most comfortable for the users. We present the results of a user study where the participants were asked how they perceived the speed and the radius of a circular moving smooth pursuit target. The data show that the users’ judgment of the optimal speed corresponds with an optimal detection rate. The results also enable us to give an optimal value pair for target speed and trajectory radius. Additionally, we give a functional relation on how to adapt the target speed when scaling the geometry to keep optimal detection rate and user experience.

One of the obstacles to bring eye tracking technology to everyday human computer interactions is the time consuming calibration procedure. In this paper we investigate a novel calibration method based on smooth pursuit eye movement. The method uses linear regression to calculate the calibration mapping. The advantage is that users can perform the calibration quickly in a few seconds and only use a small calibration area to cover a large tracking area. We first describe the theoretical background on establishing a calibration mapping and discuss differences of calibration methods used. We then present a user study comparing the new regression-based method with a classical nine-point and with other pursuit-based calibrations. The results show the proposed method is fully functional, quick, and enables accurate tracking of a large area. The method has the potential to be integrated into current eye tracking systems to make them more usable in various use cases.

In this paper we introduce a novel approach for smooth pursuits eye movement detection and demonstrate that it allows up to 160 targets to be distinguished. With this work we advance the well-established smooth pursuits technique, which allows gaze interaction without calibration. The approach is valuable for researchers and practitioners, since it enables novel user interfaces and applications to be created that employ a large number of targets, for example, a pursuits-based keyboard or a smart home where many different objects can be controlled using gaze. We present findings from two studies. In particular, we compare our novel detection algorithm based on linear regression with the correlation method. We quantify its accuracy for around 20 targets on a single circle and up to 160 targets on multiple circles. Finally, we implemented a pursuits-based keyboard app with 108 targets as proof-of-concept.

For watching omnidirectional movies via HMD, turning the head is the most relevant and natural input technique to choose the visible part of the movie. However, there is a need for additional interactivity in cinematic virtual reality (CVR), e.g. for navigating the movie, for nonlinear story lines or for communication with other viewers watching a movie together. The input device should not disturb the viewing experience and the viewer should not be primarily aware of it. We present a design space based on numerous methods in literature and our own experiences. As a result of the design space we describe interaction techniques which meet the challenges of cinematic virtual reality. For doing this, various dimensions of the design space will be combined. The most promising method, eye-based head gestures, is described in more detail and was implemented for CVR. The conducted user study will be analyzed in future work.

In this paper we present an investigation of how the speed and trajectory of smooth pursuits targets impact on detection rates in gaze interfaces. Previous work optimized these values for the specific application for which smooth pursuit eye movements were employed. However, this may not always be possible. For example UI designers may want to minimize distraction caused by the stimulus, integrate it with a certain UI element (e.g., a button), or limit it to a certain area of the screen. In these cases an in-depth understanding of the interplay between speed, trajectory, and accuracy is required. To achieve this, we conducted a user study with 15 participants who had to follow targets with different speeds and on different trajectories using their gaze. We evaluated the data with respect to detectability. As a result, we obtained reasonable ranges for target speeds and demonstrate the effects of trajectory shapes. We show that slow moving targets are hard to detect by correlation and that introducing a delay improves the detection rate for fast moving targets. Our research is complemented by design rules which enable designers to implement better pursuit detectors and pursuit-based user interfaces.