This study explores a streamlined facial data collection method for conversational contexts, addressing the limitations of existing approaches that often require extensive datasets and prioritize technical metrics over user perception and experience. We systematically investigate which facial expression data are essential for reconstructing photorealistic avatars and how they can be captured efficiently. Our research employs a two-phase methodology to identify efficient facial data collection strategies and evaluate their effectiveness. In the first phase, we conduct facial data acquisition and evaluate reconstruction performance using utterance data and emotional data. In the second phase, we carry out a comprehensive user evaluation comparing three progressive conditions: utterance only, utterance and emotional data, and a control condition involving extensive data. Findings from 24 participants engaged in simulated face-to-face conversations reveal that targeted utterance and emotional data achieve comparable levels of perceived realism, naturalness, and telepresence, while reducing training time and data usage when compared to the extensive data collection approach. These results demonstrate that targeted data inputs can enable efficient avatar face reconstruction, offering practical guidelines for real-time applications such as AR/VR telepresence and highlighting the trade-off between data quantity and perceived quality.

We present VRGaussianAvatar, an integrated system that enables real-time full-body 3D Gaussian Splatting (3DGS) avatars in virtual reality using only head-mounted display (HMD) tracking signals. The system adopts a parallel pipeline with a VR Frontend and a GA Backend. The VR Frontend uses inverse kinematics to estimate full-body pose and streams the resulting pose along with stereo camera parameters to the backend. The GA Backend stereoscopically renders a 3DGS avatar reconstructed from a single image. To improve stereo rendering efficiency, we introduce Binocular Batching, which jointly processes left and right eye views in a single batched pass to reduce redundant computation and support high-resolution VR displays. We evaluate VRGaussianAvatar with quantitative performance tests and a within-subject user study against image- and video-based mesh avatar baselines. Results show that VRGaussianAvatar sustains interactive VR performance and yields higher perceived appearance similarity, embodiment, and plausibility. Project page and source code are available at https://vrgaussianavatar.github.io.

We propose OFERA, a novel framework for real-time expression control of photorealistic Gaussian head avatars for VR headset users. Existing approaches attempt to recover occluded facial expressions using additional sensors or internal cameras, but sensor-based methods increase device weight and discomfort, while camera-based methods raise privacy concerns and suffer from limited access to raw data. To overcome these limitations, we leverage the blendshape signals provided by commercial VR headsets as expression inputs. Our framework consists of three key components: (1) Blendshape Distribution Alignment (BDA), which applies linear regression to align the headset-provided blendshape distribution to a canonical input space; (2) an Expression Parameter Mapper (EPM) that maps the aligned blendshape signals into an expression parameter space for controlling Gaussian head avatars; and (3) a Mapper-integrated Avatar (MiA) that incorporates EPM into the avatar learning process to ensure distributional consistency. Furthermore, OFERA establishes an end-to-end pipeline that senses and maps expressions, updates Gaussian avatars, and renders them in real-time within VR environments. We show that EPM outperforms existing mapping methods on quantitative metrics, and we demonstrate through a user study that the full OFERA framework enhances expression fidelity while preserving avatar realism. By enabling real-time and photorealistic avatar expression control, OFERA significantly improves telepresence in VR communication. A project page is available at https://ysshwan147.github.io/projects/ofera/.

As immersive virtual environments become more prevalent, avatars serve as critical social interfaces. This study explores how combinations of visual appearance, vocal characteristics, and informed identity influence users' initial impressions and partner preferences in four distinct XR scenarios: physical, intellectual, social, and romantic. A within-subject experiment with 40 participants assessed perceived warmth, competence, uncanniness, and selection preferences across diverse avatar configurations. Results indicate that vocal cues had a particularly strong impact on social perception, often shaping feelings of approachability and clarity in communication. While some cue alignments enhanced perceived social comfort and engagement, inconsistencies across gender-related cues occasionally led to increased perceptions of uncanniness, especially in emotionally sensitive contexts. These findings highlight the importance of designing avatars that thoughtfully adapt to different interaction contexts, supporting inclusive and responsive user experiences in social XR platforms.

As avatars have evolved from simple digital representations into extensions of our identities, they offer unprecedented opportunities for self-expression and customization beyond the physical world limitations. While virtual platforms foster new forms of identity exploration, social norms still play a crucial role in defining what is considered appropriate in these environments. In this study, we surveyed 150 participants to investigate social norms surrounding avatar modifications, examining how perspectives, contexts, and personality traits influence attitudes toward appropriateness. Our findings reveal that avatar modifications are generally viewed as more appropriate when considered from a partner's perspective, especially for changeable attributes. However, these modifications are perceived as less acceptable in professional settings such as workplaces. Additionally, individuals with high self-monitoring tendencies tend to be more resistant to changes, while those scoring higher on Machiavellianism are more accepting of changes, particularly regarding unchangeable attributes and emotional expressions. These findings provide valuable insights for platform developers and designers, highlighting the importance of implementing context-aware customization options that balance core identity elements with personality-driven preferences, thereby enhancing user experiences while respecting social norms.

Immersive technologies such as virtual and augmented reality (VR/AR) allow remote users to meet and interact in a shared virtual space using embodied virtual avatars, creating a sense of co-presence. However, asynchronous communication-essential in many real-world contexts-remains underexplored in these environments. Traditional playback-based systems lack interactivity and often fail to preserve critical contextual cues necessary for effective asynchronous communication. In this paper, we introduce AVAGENTs, AI-powered virtual avatars that replicate users' verbal and nonverbal cues from recordings of past meetings. Avagents can interpret meeting context and generate appropriate responses to questions posed by asynchronous viewers. Through a user study (N = 30), we evaluated Avagents against a traditional playback method and a voice-based AI assistant across two asynchronous meeting scenarios: analytic reasoning and affective resonance. Results showed that Avagents enhance the asynchronous communication experience by increasing social presence, sense of belonging, emotional intimacy, and other user perceptions. We discuss the findings and their implications for designing effective AI-driven asynchronous communication tools in VR/AR environments.

Understanding gender differences in facial and spatial recognition is crucial for enhancing avatar-mediated communication. However, there remains a gap in understanding how participant gender influences perceptions of avatar facial expressions and spatial dynamics in Mixed Reality communication. Therefore, our study investigates how avatar non-verbal cues interact with gender differences to affect user experience and understanding in MR environments. To examine these complex relationships, we conducted a user study comparing the effects of various avatar facial expressions (Full, Mouth-Only, and Emotion-based) and interpersonal distances (Closer vs. Farther) on facial animation realism and social presence, with a focus on gender-balanced participant groups. Our findings revealed that female participants were particularly sensitive to the avatar’s proximity and facial expressions, reporting significantly higher perceptions of facial animation realism, copresence, message understanding, and affective understanding at farther distances compared to male participants. They also perceived higher copresence and message understanding when exposed to emotion-based facial expressions, as opposed to a mouth-only condition-a distinction not observed among male participants. Based on our findings, we advocate for avatar design strategies that accommodate gender differences in perception and preference, potentially through customizable levels of expressiveness to cater to diverse user needs and contexts.

In avatar-mediated remote communication, avatars’ facial expressions can be dynamically adjusted according to each user’s computational and device constraints, highlighting the importance of varied expressions and their impact on user perception. However, there is a lack of research on how variations in avatar facial expressions, especially when simplified, influence user perception, particularly in terms of social presence. To address this, we examine the impact of various facial expression combinations on social presence in avatar-mediated communication scenarios, ranging from informative speeches to emotional conversations. Our approach involves prioritizing avatar facial blendshape combinations using two main approaches: (1) commonly activated expressions that reflect the active facial movements observed during casual conversations, and (2) emotion-based expressions derived from Facial Action Coding System (FACS). These combinations were compared against minimal baseline and full blendshape conditions through a comprehensive study involving 32 participants. Our findings reveal that emotion-based condition achieves comparable levels of social presence and communication quality to the full condition, in both informative speeches and emotional conversations. This highlights the effectiveness of prioritizing emotion-based expressions and adopting a streamlined approach to avatar facial control. By focusing on emotional expressions while optimizing resources, this approach shows potential for enhancing the avatar-mediated communication experience, accommodating the diverse users’ contexts.