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

TL;DR

This paper introduces VALID, a validated 3D avatar library emphasizing racial diversity and inclusion, with statistically confirmed perceptions of race and gender from a large international online study.

Contribution

It presents the first validated, diverse avatar library for social computing research, including a detailed creation process and perception validation data.

Findings

Avatars of certain races are more accurately identified by same-race participants.

The library includes 210 fully rigged avatars focused on diversity and inclusion.

Statistically validated labels for perceived race and gender are provided.

Abstract

As consumer adoption of immersive technologies grows, virtual avatars will play a prominent role in the future of social computing. However, as people begin to interact more frequently through virtual avatars, it is important to ensure that the research community has validated tools to evaluate the effects and consequences of such technologies. We present the first iteration of a new, freely available 3D avatar library called the Virtual Avatar Library for Inclusion and Diversity (VALID), which includes 210 fully rigged avatars with a focus on advancing racial diversity and inclusion. We present a detailed process for creating, iterating, and validating avatars of diversity. Through a large online study (n=132) with participants from 33 countries, we provide statistically validated labels for each avatar's perceived race and gender. Through our validation study, we also advance knowledge pertaining to the perception of an avatar's race. In particular, we found that avatars of some races were more accurately identified by participants of the same race.