Individual's Social Perception of Virtual Avatars Embodied with Their Habitual Facial Expressions and Facial Appearance.

embodied conversational agent empathy social interaction virtual avatar virtual character virtual human

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
06 Sep 2021
Historique:
received: 20 08 2021
revised: 03 09 2021
accepted: 04 09 2021
entrez: 10 9 2021
pubmed: 11 9 2021
medline: 14 9 2021
Statut: epublish

Résumé

With the prevalence of virtual avatars and the recent emergence of metaverse technology, there has been an increase in users who express their identity through an avatar. The research community focused on improving the realistic expressions and non-verbal communication channels of virtual characters to create a more customized experience. However, there is a lack in the understanding of how avatars can embody a user's signature expressions (i.e., user's habitual facial expressions and facial appearance) that would provide an individualized experience. Our study focused on identifying elements that may affect the user's social perception (similarity, familiarity, attraction, liking, and involvement) of customized virtual avatars engineered considering the user's facial characteristics. We evaluated the participant's subjective appraisal of avatars that embodied the participant's habitual facial expressions or facial appearance. Results indicated that participants felt that the avatar that embodied their habitual expressions was more similar to them than the avatar that did not. Furthermore, participants felt that the avatar that embodied their appearance was more familiar than the avatar that did not. Designers should be mindful about how people perceive individuated virtual avatars in order to accurately represent the user's identity and help users relate to their avatar.

Identifiants

pubmed: 34502877
pii: s21175986
doi: 10.3390/s21175986
pmc: PMC8434682
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Research Foundation of Korea
ID : 2020R1A2B5B02002770

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Auteurs

Sung Park (S)

School of Design, Savannah College of Art and Design, Savannah, GA 31401, USA.

Si Pyoung Kim (SP)

Department of Emotion Engineering, Sangmyung University, Jongno-gu, Seoul 03016, Korea.

Mincheol Whang (M)

Department of Human Centered Artificial Intelligence, Sangmyung University, Jongno-gu, Seoul 03016, Korea.

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Classifications MeSH