VALID: A perceptually validated Virtual Avatar Library for Inclusion and Diversity

Tiffany Do
Steve Zelenty
Ryan P McMahan


With the arrival of immersive technologies, virtual avatars have gained a prominent role in the future of social computing. However, there is a lack of free resources that can provide researchers with diverse sets of virtual avatars, and the few that are available have not been validated. In this paper, we present VALID a new, freely available 3D avatar library. VALID includes 210 fully rigged avatars that were modeled through an iterative design process and represent the seven ethnicities recommended by U.S. Census Bureau research. We validated the avatars through a user study with participants (n = 132) from 33 countries, and provide statistically validated labels for each avatar’s perceived ethnicity and gender. Through our validation, we also advance the understanding of avatar ethnicity and show it can replicate the human psychology phenomenon of own-race bias in face recognition.