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Virtual Big Heads in Extended Reality: Estimation of Ideal Head Scales and Perceptual Thresholds for Comfort and Facial Cues

Extended reality (XR) technologies, such as virtual reality (VR) and augmented reality (AR), provide users, their avatars, and embodied agents a shared platform to collaborate in a spatial context. While traditional face-to-face communication is limited by users’ proximity, meaning that another human’s non-verbal embodied cues become more difficult to perceive the farther one is away from that person, researchers and practitioners have started to look into ways to accentuate or amplify such embodied cues and signals to counteract the effects of distance with XR technologies. In this paper, we describe and evaluate the “Big Head” technique, in which a human’s head in VR/AR is scaled up relative to their distance from the observer as a mechanism for enhancing the visibility of non-verbal facial cues, such as facial expressions or eye gaze. To better understand and explore this technique, we present two complimentary human-subject experiments in this paper. In our first experiment, we conducted a VR study with a head-mounted display (HMD) to understand the impact of increased or decreased head scales on participants’ ability to perceive facial expressions as well as their sense of comfort and feeling of “uncannniness” over distances of up to 10 meters. We explored two different scaling methods and compared perceptual thresholds and user preferences. Our second experiment was performed in an outdoor AR environment with an optical see-through (OST) HMD. Participants were asked to estimate facial expressions and eye gaze, and identify a virtual human over large distances of 30, 60, and 90 meters. In both experiments, our results show significant differences in minimum, maximum, and ideal head scales for different distances and tasks related to perceiving faces, facial expressions, and eye gaze, while we also found that participants were more comfortable with slightly bigger heads at larger distances. We discuss our findings with respect to the technologies used, and we discuss implications and guidelines for practical applications that aim to leverage XR-enhanced facial cues.