Do We Measure What We Perceive? Comparison of Perceptual and Computed Differences between Hand Animations

BSTRACT An increased interest in public motion capture data has allowed for the use of data-driven animation algorithms through neural networks. While motion capture data is increasingly accessible, data sets have become too large to sort through manually. Similarity metrics quantify how different two motions are and can be used to search databases much faster when compared to manual searches as well as to train neural networks. However, the most popular similarity metrics are not informed by human perception, resulting in the potential for data that is not perceptually similar being labeled as such by these metrics. We conducted an experiment with hand motions to identify how large the differences between human perception and common similarity metrics are. In this study, participants watched two animations of hand motions, one altered and the other unaltered, and scored their similarity on a 7-point Likert scale. In our comparisons, we found that none of the tested similarity metrics correlated with human judged scores of similarity.