Personality Transfer in Human Animation: Handcrafted versus Data-Driven Approaches

Personality is the individual's interrelated behavioral and emotional patterns that form the unique self. Personality-enriched animations benefit digital characters, which helps improve realism and communication. Body movements, among other modalities, can include strong cues for personality expression. We focus on altering the body movements of human animation to express the desired personality traits following two approaches: (i) a traditional approach that utilizes handcrafted motion adjustments following heuristic rules and (ii) a data-driven approach that separates content and personality into different latent spaces to reconstruct the same motion with altered personality. While the sample size does not affect the traditional approach, the scarcity of personality-labeled animation datasets prevents using sophisticated data-driven models; to this end, we utilize Neural Motion Fields (NeMF) in our data-driven personality transfer architecture. We evaluate the performance of the two approaches through a three-part user study; different models stand out for altering specific personality factors.