Creation and validation of the library

Our initial selection of races and ethnicities for the diverse avatar library follows the most recent guidelines of the US Census Bureau that as of 2023 recommended the use of 7 ethnic and racial groups representing a large demographic of the US society, which can also be extrapolated to the global population. These groups include Hispanic or Latino, American Indian or Alaska Native (AIAN), Asian, Black or African American, Native Hawaiian or Other Pacific Islander (NHPI), White, Middle East or North Africa (MENA). We envision the library will continue to evolve to bring even more diversity and representation with future additions of avatars.

The avatars were hand modeled and created using a process that combined average facial features with extensive collaboration with representative stakeholders from each racial group, where their feedback was used to artistically modify the facial mesh of the avatars. Then we conducted an online study with participants from 33 countries to determine whether the race and gender of each avatar in the library are recognizable. In addition to the avatars, we also provide labels statistically validated through observation of users for the race and gender of all 42 base avatars (see below).

Example of the headshots of a Black/African American avatar presented to participants during the validation of the library.

We found that all Asian, Black, and White avatars were universally identified as their modeled race by all participants, while our American Indian or Native Alaskan (AIAN), Hispanic, and Middle Eastern or North African (MENA) avatars were typically only identified by participants of the same race. This also indicates that participant race can improve identification of a virtual avatar of the same race. The paper accompanying the library release highlights how this ingroup familiarity should also be taken into account when studying avatar behavior in VR.

Confusion matrix heatmap of agreement rates for the 42 base avatars separated by other-race participants and same-race participants. One interesting aspect visible in this matrix, is that participants were significantly better at identifying the avatars of their own race than other races.

Dataset details

Our models are available in FBX format, are compatible with previous avatar libraries like the commonly used Rocketbox, and can be easily integrated into most game engines such as Unity and Unreal. Additionally, the avatars come with 69 bones and 65 facial blendshapes to enable researchers and developers to easily create and apply dynamic facial expressions and animations. The avatars were intentionally made to be partially cartoonish to avoid extreme look-a-like scenarios in which a person could be impersonated, but still representative enough to be able to run reliable user studies and social experiments.

Images of the skeleton rigging (bones that allow for animation) and some facial blend shapes included with the VALID avatars.

The avatars can be further combined with variations of casual attires and five professional attires, including medical, military, worker and business. This is an intentional improvement from prior libraries that in some cases reproduced stereotypical gender and racial bias into the avatar attires, and provided very limited diversity to certain professional avatars.

Images of some sample attire included with the VALID avatars.

Get started with VALID

We believe that the Virtual Avatar Library for Inclusion and Diversity (VALID) will be a valuable resource for researchers and developers working on VR/AR applications. We hope it will help to create more inclusive and equitable virtual experiences. To this end, we invite you to explore the avatar library, which we have released under the open source MIT license. You can download the avatars and use them in a variety of settings at no charge.

Acknowledgements

This library of avatars was born out of a collaboration with Tiffany D. Do, Steve Zelenty and Prof. Ryan P McMahan from the University of Central Florida.