[2512.16081] Evaluation of Generative Models for Emotional 3D Animation Generation in VR

Computer Science > Human-Computer Interaction arXiv:2512.16081 (cs) [Submitted on 18 Dec 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Evaluation of Generative Models for Emotional 3D Animation Generation in VR Authors:Kiran Chhatre, Renan Guarese, Andrii Matviienko, Christopher Peters View a PDF of the paper titled Evaluation of Generative Models for Emotional 3D Animation Generation in VR, by Kiran Chhatre and 3 other authors View PDF HTML (experimental) Abstract:Social interactions incorporate 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 model effectiveness. To address this, we evaluate emotional 3D animation generative models within a 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 examine perceived emotional quality for three state of the art speech-driven 3D animation methods across two emotions happiness (high arousal) and neutral (mid arousal). Additionally, we compare these generative models against real human expressions obtained via a reconstruction-...