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The expression of human emotion is integral to social interaction, and in virtual reality it is increasingly common to develop virtual avatars that attempt to convey emotions by mimicking these visual and aural cues, i.e. the facial and vocal expressions. However, errors in (or the absence of) facial tracking can result in the rendering of incorrect facial expressions on these virtual avatars. For example, a virtual avatar may speak with a happy or unhappy vocal inflection while their facial expression remains otherwise neutral. In circumstances where there is conflict between the avatar's facial and vocal expressions, it is possible that users will incorrectly interpret the avatar's emotion, which may have unintended consequences in terms of social influence or in terms of the outcome of the interaction. In this paper, we present a human-subjects study (N = 22) aimed at understanding the impact of conflicting facial and vocal emotional expressions. Specifically we explored three levels of emotional valence (unhappy, neutral, and happy) expressed in both visual (facial) and aural (vocal) forms. We also investigate three levels of head scales (down-scaled, accurate, and up-scaled) to evaluate whether head scale affects user interpretation of the conveyed emotion. We find significant effects of different multimodal expressions on happiness and trust perception, while no significant effect was observed for head scales. Evidence from our results suggest that facial expressions have a stronger impact than vocal expressions. Additionally, as the difference between the two expressions increase, the less predictable the multimodal expression becomes. For example, for the happy-looking and happy-sounding multimodal expression, we expect and see high happiness rating and high trust, however if one of the two expressions change, this mismatch makes the expression less predictable. We discuss the relationships, implications, and guidelines for social applications that aim to leverage multimodal social 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. Although 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 article, 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 article. In our first experiment, we conducted a VR study with a head-mounted display 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 m. 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 head-mounted display. Participants were asked to estimate facial expressions and eye gaze, and identify a virtual human over large distances of 30, 60, and 90 m. 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, and 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. 

Due to the additive light model employed by current optical see-through head-mounted displays (OST-HMDs), the perceived contrast of displayed imagery is reduced with increased environment luminance, often to the point where it becomes difficult for the user to accurately distinguish the presence of visual imagery. While existing contrast models, such as Weber contrast and Michelson contrast, can be used to predict when the observer will experience difficulty distinguishing and interpreting stimuli on traditional dis-plays, these models must be adapted for use with additive displays. In this paper, we present a simplified model of luminance contrast for optical see-through displays derived from Michelson's contrast equation and demonstrate two applications of the model: informing design decisions involving the color of virtual imagery and optimizing environment light attenuation through the use of neutral density filters. 

In a future of pervasive augmented reality (AR), AR systems will need to be able to efficiently draw or guide the attention of the user to visual points of interest in their physical-virtual environment. Since AR imagery is overlaid on top of the user's view of their physical environment, these attention guidance techniques must not only compete with other virtual imagery, but also with distracting or attention-grabbing features in the user's physical environment. Because of the wide range of physical-virtual environments that pervasive AR users will find themselves in, it is difficult to design visual cues that “pop out” to the user without performing a visual analysis of the user's environment, and changing the appearance of the cue to stand out from its surroundings. In this paper, we present an initial investigation into the potential uses of dichoptic visual cues for optical see-through AR displays, specifically cues that involve having a difference in hue, saturation, or value between the user's eyes. These types of cues have been shown to be preattentively processed by the user when presented on other stereoscopic displays, and may also be an effective method of drawing user attention on optical see-through AR displays. We present two user studies: one that evaluates the saliency of dichoptic visual cues on optical see-through displays, and one that evaluates their subjective qualities. Our results suggest that hue-based dichoptic cues or “Forbidden Colors” may be particularly effective for these purposes, achieving significantly lower error rates in a pop out task compared to value-based and saturation-based cues. 

Display technologies in the fields of virtual and augmented reality affect the appearance of human representations, such as avatars used in telepresence or entertainment applications, based on the user’s current viewing conditions. With changing viewing conditions, it is possible that the perceived appearance of one’s avatar changes in an unexpected or undesired manner, which may change user behavior towards these avatars and cause frustration in using the AR display. In this paper, we describe a user study (N=20) where participants saw themselves in a mirror standing next to their own avatar through use of a HoloLens 2 optical see-through head-mounted display. Participants were tasked to match their avatar’s appearance to their own under two environment lighting conditions (200 lux and 2,000 lux). Our results showed that the intensity of environment lighting had a significant effect on participants selected skin colors for their avatars, where participants with dark skin colors tended to make their avatar’s skin color lighter, nearly to the level of participants with light skin color. Further, in particular female participants made their avatar’s hair color darker for the lighter environment lighting condition. We discuss our results with a view on technological limitations and effects on the diversity of avatar representations on optical see-through displays. 

Given the inherent visual affordances of Head-Mounted Displays (HMDs) used for Virtual and Augmented Reality (VR/AR), they have been actively used over many years as assistive and therapeutic devices for the people who are visually impaired. In this paper, we report on a scoping review of literature describing the use of HMDs in these areas. Our high-level objectives included detailed reviews and quantitative analyses of the literature, and the development of insights related to emerging trends and future research directions. Our review began with a pool of 1251 papers collected through a variety of mechanisms. Through a structured screening process, we identified 61 English research papers employing HMDs to enhance the visual sense of people with visual impairments for more detailed analyses. Our analyses reveal that there is an increasing amount of HMD-based research on visual assistance and therapy, and there are trends in the approaches associated with the research objectives. For example, AR is most often used for visual assistive purposes, whereas VR is used for therapeutic purposes. We report on eight existing survey papers, and present detailed analyses of the 61 research papers, looking at the mitigation objectives of the researchers (assistive versus therapeutic), the approaches used, the types of HMDs, the targeted visual conditions, and the inclusion of user studies. In addition to our detailed reviews and analyses of the various characteristics, we present observations related to apparent emerging trends and future research directions.