Avatars play a central role in virtual and augmented reality (VR/AR), yet little is known about how they are represented in research. To address this gap, we collected and analyzed 14 440 avatar images from 4 659 publications. Every image was hand-labeled for gender, ethnicity, body representation, and visual style, and linked to publication metadata such as keywords, affiliation, and year. Our large-scale analysis yields 14 findings: for example, the dominance of realistic white male avatars in VR studies, the use of lower-fidelity and more gender-diverse bodies in AR, the post-2021 rise of stylized designs and diversity labels, and the mismatch between the amount of female keywords and their avatar occurrences. Based on our findings, we offer practical implications for avatar designers and researchers, such as adopting balanced starter libraries, employing bias dashboards, and using simple representation checklists. These steps can help future VR and AR platforms reduce bias and improve representations in avatar systems.

Social Virtual Reality (VR) platforms have surged in popularity in recent years, including among people with disabilities (PWD). Previous research has documented accessibility challenges, harassment, and negative experiences for PWD using disability signifiers in VR, primarily focusing on those with visible disabilities who encounter negative experiences. Yet, little is known about the experiences of people with invisible disabilities in social VR environments, and whether positive experiences are also common. To address these gaps, we designed inclusive avatars (avatars with disability signifiers) and investigated the lived experiences of 26 individuals with both visible and invisible disabilities immersing themselves in social interactions in VRChat for a week. We utilized a mixed methods experience sampling design and multilevel regression to explore the relationships between social interactions of PWD in VR and various psychological outcomes. Our results indicate that PWD, both visible and invisible, experienced positive and negative social interactions in VR. These interactions, in turn, significantly influenced users’ overall experience with inclusive avatars, affecting aspects such as emotional responses, engagement levels, satisfaction with the avatar’s design, and perceptions of inclusion in VR. Qualitative interviews of 18 participants allowed for a more nuanced exploration of the experiences of PWD by giving voice to users who are rarely studied in depth. Findings provided unique insights into both the positive and negative experiences of PWD, as well as identified key design factors influencing user experience in social VR.

User avatars are a critical component in collaborative Virtual Environments. A multitude of tools exist, employing various avatar types for self-representation and the representation of others. However, the optimal appearance for these avatars remains unclear. Consumer applications predominantly utilize stylized avatars, which are less prevalent in enterprise sectors. To investigate users’ avatar preferences, we conducted three user studies in both non-immersive and immersive environments, exploring whether social contexts influence virtual self-avatar selection. We generated individual avatars based on photographs of 91 participants, comprising 71 employees in the automotive industry and 20 individuals external to the company. Our findings indicate that work situations, irrespective of the occupational domain, significantly impact self-avatar choices. Conversely, we observed no correlation between the display medium or personality dimensions and avatar selection. Drawing upon our results, we provide recommendations for future avatar representation in work environments.

At exhibitions, visitors are usually in a completely unknown environment. Although visitors generally are informed about the topic before a visit, interests are still difficult to extract from the mass of exhibition stands and offers. In this paper we describe a concept using head-coupled AR together with recommender mechanisms for exhibitions. We present a conceptual development for a first prototype with focus on navigational aspects as well as explicit and implicit recommendations to generate input data for visually displayed recommendations.