Evaluation of generative models for emotional 3D animation generation in VR - Chhatre, Kiran; Guarese, Renan; Matviienko, Andrii; Peters, Christopher (Frontiers in Computer Science, 2025)
IntroductionSocial interactions incorporate various nonverbal signals to convey emotions alongside speech, including facial expressions and body gestures. Generative models have demonstrated promising results in creating full-body nonverbal animations synchronized with speech; however, evaluations using statistical metrics in 2D settings fail to fully capture user-perceived emotions, limiting our understanding of the effectiveness of these models.MethodsTo address this, we evaluate emotional 3D animation generative models within an immersive Virtual Reality (VR) environment, emphasizing user—centric metrics-emotional arousal realism, naturalness, enjoyment, diversity, and interaction quality—in a real-time human-agent interaction scenario. Through a user study (N = 48), we systematically examine perceived emotional quality for three state-of-the-art speech-driven 3D animation methods across two specific emotions: happiness (high arousal) and neutral (mid arousal). Additionally, we compare these generative models against real human expressions obtained via a reconstruction-based method to assess both their strengths and limitations and how closely they replicate real human facial and body expressions.ResultsOur results demonstrate that methods explicitly modeling emotions lead to higher recognition accuracy compared to those focusing solely on speech-driven synchrony. Users rated the realism and naturalness of happy animations significantly higher than those of neutral animations, highlighting the limitations of current generative models in handling subtle emotional states.DiscussionGenerative models underperformed compared to reconstruction-based methods in facial expression quality, and all methods received relatively low ratings for animation enjoyment and interaction quality, emphasizing the importance of incorporating user-centric evaluations into generative model development. Finally, participants positively recognized animation diversity across all generative models.