Virtual Reality (VR) is transforming cognitive and psychological research by enabling immersive simulations that elicit authentic emotional responses. The high demand for VR-based emotion recognition is also evident in fields such as mental healthcare, education, and entertainment, where understanding users' emotional states can enhance user experience and system effectiveness. However, the lack of comprehensive datasets hinders progress in VR-based emotion recognition. In this paper, we present a comprehensive, multi-faceted eye-tracking dataset collected from 26 participants using 28 emotional video stimuli rendered in a custom virtual environment. Our dataset is the first to incorporate high-frame-rate periocular videos, capturing subtle motions, such as micro-expressions and eyebrow shifts, which are critical for emotion analysis. Additionally, it includes high-frequency eye-tracking data, offering gaze direction and pupil dynamics at four times the frequency of existing datasets. Our dataset is also unique in providing emotion annotations according to Ekman's emotion model and, as such, offering experiments impossible using existing datasets. Our benchmark evaluations show that fusing the multi-faceted eye-tracking signals in our dataset significantly improves emotion recognition accuracy. As such, our work has the potential to significantly accelerate and enable entirely new research on emotion-aware VR applications.

Gaze information provides indication of users focus which complements remote collaboration tasks, as distant users can see their partner’s focus. In this paper, we apply gaze for co-located collaboration, where users’ gaze locations are presented on the same display, to help collaboration between partners. We integrated various types of gaze indicators on the user interface of a collaborative search system, and we conducted two user studies to understand how gaze enhances coordination and communication between co-located users. Our results show that gaze indeed enhances co-located collaboration, but with a trade-off between visibility of gaze indicators and user distraction. Users acknowledged that seeing gaze indicators eases communication, because it let them be aware of their partner’s interests and attention. However, users can be reluctant to share their gaze information due to trust and privacy, as gaze potentially divulges their interests.