Towards understanding personality expression via body motion

Abstract

This work addresses the challenge of data scarcity in personality-labeled datasets by introducing personality labels to clips from two open datasets, ZeroEGGS and Bandai, which provide diverse full-body animations. To this end, we present a user study to annotate short clips from both sets with labels based on the Five-Factor Model (FFM) of personality. We chose features informed by Laban Movement Analysis (LMA) to represent each animation. These features then guided us to select the samples of distinct motion styles to be included in the user study, obtaining high personality variance and keeping the study duration and cost viable. Using the labeled data, we then ran a correlation analysis to find features that indicate high correlation with each personality dimension. Our regression analysis results indicate that highly correlated features are promising in accurate personality estimation. We share our early findings, code, and data publicly.