Older adults, a growing demographic, face an increased risk of experiencing loneliness and are less exposed to emerging communication technologies. Augmented reality (AR) avatars and telepresence robots have been proposed as tools to foster social connection, yet their suitability for older users remains underexplored. We present an exploratory study with ten healthy older adults who engaged in both conversational and spatial collaboration tasks using AR avatar-mediated communication, robot-mediated communication, and face-to-face interaction. We collected self-reported measures of co-presence, social presence, closeness, uncanny valley, preferences, and open feedback. Our findings suggest that telepresence robots enhanced co-presence, while avatars were valued for their expressivity and humanlike qualities. Task type influenced co-presence in spatial collaboration only during communication using the telepresence robot. Other measures, such as social presence and closeness, were unaffected by task type or representation. While neither technology outperformed face-to-face interaction, both were positively received, underscoring their potential to address the social needs of older adults and highlighting the importance of enhancing nonverbal expressivity, particularly nonverbal cues in mediated communication. Ultimately, our results contribute to the fundamental understanding of mediated communication with older adults, motivating further empirical work to confirm and extend these findings.

Avatars and telepresence robots have long received attention for remote communication. However, the specific nature of their physicality, expressiveness, and mobility may affect their usefulness for different tasks. This work compares using an avatar (presented in augmented reality) and a telepresence robot to Face-to-Face (F2F) communication during different communication tasks: free conversation, negotiation, and referential communication with movement. We conducted a user study (split-plot design, N=54) with the type of representation of the conversational partner as the within variable and the communication task as the between variable. Our results show that the type of task, especially referential communication with movement, influenced the perceived attention to nonverbal cues and closeness. Generally, gestures and body movements received the least focus with telepresence robots. Gestures in avatars and F2F drew similar attention, which we attribute to the avatar's tracking fidelity. Gaze received less attention in both avatar- and robot-mediated communication compared to F2F, while facial expressions on the robot's screen heightened attention compared to avatars. These findings advance the fundamental understanding of mediated communication and support researchers and practitioners in shaping the design of communication applications beyond today's video calls.

Abstract Behavioral realism plays a crucial role in virtual and augmented reality (VR/AR). Various avatar animation techniques, ranging from full-body motion capture to single-point inverse kinematics (IK), offer different levels of realism. While the animation of a user’s own avatar influences embodiment, the perceived realism of others’ avatars is equally important for immersion. This study ( N = 53) examines how users in smartphone AR, head-mounted display (HMD) AR, and VR perceive the behavioral realism of avatars animated with single-point IK compared to those driven by full-body motion capture. In addition, we explore whether the congruence between visual fidelity of an avatar and tracking accuracy affects perception. Our findings indicate that full-body motion capture produces significantly higher perceived realism than single-point IK, but the type of device does not have measurable impact. Furthermore, while congruence between visual realism and tracking fidelity was expected to play a role, our results suggest that its influence is limited. Despite lower realism than motion capture, modern IK techniques are still perceived positively, highlighting their viability for multi-user AR and VR applications.

Behavioral realism is a key element in social virtual and augmented reality (VR/AR). Different techniques, from full-body to single-point IK, are used to animate virtual humans, providing varying levels of behavioral realism. While the animation method of the user’s own avatar is important, e.g., considering embodiment, the animation of others is equally important to display a convincing immersive experience. This study ($\mathrm{N}=36$) investigates how users (in AR or VR) rate the behavioral realism of others when the other person’s avatar is animated using a single point Inverse Kinematics (IK) and compares results to a full-body motion-capture-driven avatar. In addition, we investigate whether congruence between visual realism and tracking quality matters. Our results reveal that animations based on full-body motion capture are found to result in higher perceived behavioral realism compared to those using single-point inverse kinematics (IK), that device type has no influence, and that congruence seems to matter less. Despite not reaching the same level of realism as motion-captured animations, our results suggest the applicability of state-of-the-art IK techniques for multi-user applications in AR and VR as, in general, participants rated both positively.

Augmented Reality (AR) and Virtual Reality (VR) are essential tools for researchers and practitioners, serving purposes from training to entertainment: many of these applications rely on agents. This study explores the impact of agent characteristics on user reactions, focusing on gaze as a primary visual attention indicator in AR and VR. While existing research has investigated the agent’s gaze and its influence on the user, it is unclear how the agent’s auralization and visualization influence gaze behaviour. We investigate this by studying the impact of rendering style and type of audio on gaze behaviour during a narrative AR experience. Participants listened to a story with the agent visualized as a cartoon-style or realistic virtual human and auralized with spatial or non-spatial audio. The results revealed that the agent’s rendering style significantly influenced gaze behaviour, with cartoon-style agents capturing more visual attention. Audio variations did not yield significant differences. Together, our findings inform the design of AR user interfaces with agents, suggesting that low-realism visualizations are more captivating and, thus, more suitable for experiences where the user is supposed to look at the storyteller.

Given the profound impact of staying socially connected on the well-being of older adults, this study explores the potential of augmented reality (AR) systems to enrich their social lives. A wearable AR communication system prototype was developed and tested in a user study involving N = 16 older adults from Germany. Participants wore an AR headset and engaged in a conversation task with a remote person represented by an avatar. Older adults’ experiences were assessed using think-aloud protocols, qualitative observations, posttest questionnaires, and semi-structured oral interviews. Preliminary findings indicate overall participant satisfaction, with minimal observed difficulties in headset usage and avatar-mediated interpersonal communication. The positive engagement during AR conversations highlights the system’s potential to provide positive communication experiences among older individuals. This work-in-progress paper introduces the developed system prototype and outlines the conducted user study. Further data analyses will provide deeper insights into older adults’ experiences with the system. The results will contribute to refining the prototype and offer valuable insights for the development of AR communication systems tailored to the needs and preferences of older adults.

The appearance of virtual humans (avatars and agents) has been widely explored in immersive environments. However, virtual humans’ movements and associated sounds in real-world interactions, particularly in Augmented Reality (AR), are yet to be explored. In this paper, we investigate the influence of three distinct movement patterns (circle, side-to-side, and standing), two rendering styles (realistic and cartoon), and two types of audio (spatial audio and non-spatial audio) on emotional responses, social presence, appearance and behavior plausibility, audiovisual coherence, and auditory plausibility. To enable that, we conducted a study (N=36) where participants observed an agent reciting a short fictional story. Our results indicate an effect of the rendering style and the type of movement on the subjective perception of the agents behaving in an AR environment. Participants reported higher levels of excitement when they observed the realistic agent moving in a circle compared to the cartoon agent or the other two movement patterns. Moreover, we found an influence of agent’s movement pattern on social presence and higher appearance and behavior plausibility for the realistic rendering style. Regarding audiovisual spatial coherence, we found an influence of rendering style and type of audio only for the cartoon agent. Additionally, the spatial audio was perceived as more plausible than non-spatial audio. Our findings suggest that aligning realistic rendering styles with realistic auditory experiences may not be necessary for 1-1 listening experiences with moving sources. However, movement patterns of agents influence excitement and social presence in passive unidirectional communication scenarios.

Augmented reality (AR) presents vivid opportunities for interpersonal communication. With the growing diversity of social AR users, understanding their unique needs and perceptions becomes crucial. This study delves into how younger, middle-aged, and older adults perceive avatars with different aging attributes and degrees of realism, focusing on their anticipated user experience within a social AR system. We conducted an online within-subjects experiment involving $N = 2086$ age-diverse participants from Germany who assessed a set of nine gender-matched avatars for their perceived social attractiveness (research question 1 = RQ1) and the likelihood of selecting these avatars for self-representation in social AR (RQ2). The evaluated avatars represented different age groups (younger, middle-aged, and older) and levels of realism (low, medium, and high). We validated both the created avatars and our experimental setup and employed a linear mixed-effects modeling approach to analyze the data. Our findings unveiled a strong preference for younger high-realism avatars as communication partners (RQ1), which was consistent across all participant age groups. Similarly, participants favored younger high-realism avatars for self-representation in social AR (RQ2). However, older adults were more inclined to opt for avatars resembling their actual age. The study highlights the prevalence of age-related stereotypes in avatar-based communication. Similar to face-to-face social interactions, these stereotypes tend to render older avatars less socially attractive than their younger counterparts, irrespective of the avatar’s degree of realism. Our results invite considerations on how to combat these stereotypes through a more thoughtful and inclusive avatar design process that encompasses a broader spectrum of aging attributes.

Augmented Reality (AR) and Virtual Reality (VR) are pushing from the labs towards consumers, especially with social applications. These applications require visual representations of humans and intelligent entities. However, displaying and animating photo-realistic models comes with a high technical cost while low-fidelity representations may evoke eeriness and overall could degrade an experience. Thus, it is important to carefully select what kind of avatar to display. This article investigates the effects of rendering style and visible body parts in AR and VR by adopting a systematic literature review. We analyzed 72 papers that compare various avatar representations. Our analysis includes an outline of the research published between 2015 and 2022 on the topic of avatars and agents in AR and VR displayed using head-mounted displays, covering aspects like visible body parts (e.g., hands only, hands and head, full-body) and rendering style (e.g., abstract, cartoon, realistic); an overview of collected objective and subjective measures (e.g., task performance, presence, user experience, body ownership); and a classification of tasks where avatars and agents were used into task domains (physical activity, hand interaction, communication, game-like scenarios, and education/training). We discuss and synthesize our results within the context of today's AR and VR ecosystem, provide guidelines for practitioners, and finally identify and present promising research opportunities to encourage future research of avatars and agents in AR/VR environments.

Semantic segmentation aims at providing a fine-grained image prediction by assigning each pixel to a specific semantic category. Convolutional neural networks offer significant benefits for solving this problem. However, the success of such networks is closely related to the availability of corresponding data sets. To facilitate semantic segmentation in a broader range of scenarios, such as augmented reality in outdoor environments or universal image-to-image translation, adequate training data sets are necessary. We present OUTSIDE15k, a large-scale data set for semantic segmentation of universal outdoor scenes. The data is labeled with 24 different semantic classes. The images contain multiple outdoor scenarios and cover a variety of different resolutions. Additionally, we present OUTSIDE-Net, an improved neural network architecture integrating multi-level pooling, feature fusion, and a spatial mask for semantic segmentation of universal outdoor scenes. It extracts spatial and semantic features from the input images to perform the segmentation. With the presented data set, we show the capability of our network which outperforms state-of-the-art approaches by achieving up to 91.5% pixel accuracy.

Video surveillance is becoming increasingly common, but raises serious questions related to data confidentiality and privacy issues. In order to address these issues, several approaches for selective video encryption have been proposed within recent years: They aim at encrypting specific video regions, while keeping the remaining video unencrypted for analysis purposes. This paper describes a new system for selective H.264 video encryption which, in contrast to other approaches, individually encrypts and decrypts several regions in the video, uses a well-accepted encryption standard, and allows playback of the partially encrypted videos using standard H.264 decoders.