Avatar Visual Similarity for Social HCI: Increasing Self-Awareness
Bernhard Hilpert, Claudio Alves da Silva, Leon Christidis, Chirag Bhuvaneshwara, Patrick Gebhard, Fabrizio Nunnari, Dimitra Tsovaltzi
TL;DR
The paper investigates how avatar visual similarity affects self-awareness in virtual training. It introduces a theory-based methodology to systematically vary facial similarity across 0%, 50%, and 100% using open-access tools FaceGen and MetaHuman, evaluated in a within-subject study (N=33). Results show that increasing similarity influences perceived similarity, explicit identification, and implicit affinity, with effects varying across manipulation levels and moderated by factors like realism. The work provides a replicable pipeline for generating controlled avatar variations and demonstrates that nuanced, non-generic avatars can better elicit self-awareness in immersive training contexts, guiding future mixed-reality design and evaluation.
Abstract
Self-awareness is a critical factor in social human-human interaction and, hence, in social HCI interaction. Increasing self-awareness through mirrors or video recordings is common in face-to-face trainings, since it influences antecedents of self-awareness like explicit identification and implicit affective identification (affinity). However, increasing self-awareness has been scarcely examined in virtual trainings with virtual avatars, which allow for adjusting the similarity, e.g. to avoid negative effects of self-consciousness. Automatic visual similarity in avatars is an open issue related to high costs. It is important to understand which features need to be manipulated and which degree of similarity is necessary for self-awareness to leverage the added value of using avatars for self-awareness. This article examines the relationship between avatar visual similarity and increasing self-awareness in virtual training environments. We define visual similarity based on perceptually important facial features for human-human identification and develop a theory-based methodology to systematically manipulate visual similarity of virtual avatars and support self-awareness. Three personalized versions of virtual avatars with varying degrees of visual similarity to participants were created (weak, medium and strong facial features manipulation). In a within-subject study (N=33), we tested effects of degree of similarity on perceived similarity, explicit identification and implicit affective identification (affinity). Results show significant differences between the weak similarity manipulation, and both the strong manipulation and the random avatar for all three antecedents of self-awareness. An increasing degree of avatar visual similarity influences antecedents of self-awareness in virtual environments.