EmoDiffGes: Emotion‐Aware Co‐Speech Holistic Gesture Generation with Progressive Synergistic Diffusion

Abstract

Abstract Co‐speech gesture generation, driven by emotional expression and synergistic bodily movements, is essential for applications such as virtual avatars and human‐robot interaction. Existing co‐speech gesture generation methods face two fundamental limitations: (1) producing inexpressive gestures due to ignoring the temporal evolution of emotion; (2) generating incoherent and unnatural motions as a result of either holistic body oversimplification or independent part modeling. To address the above limitations, we propose EmoDiffGes, a diffusion‐based framework grounded in embodied emotion theory, unifying dynamic emotion conditioning and part‐aware synergistic modeling. Specifically, a Dynamic Emotion‐Alignment Module (DEAM) is first applied to extract dynamic emotional cues and inject emotion guidance into the generation process. Then, a Progressive Synergistic Gesture Generator (PSGG) iteratively refines region‐specific latent codes while maintaining full‐body coordination, leveraging a Body Region Prior for part‐specific encoding and Progressive Inter‐Region Synergistic Flow for global motion coherence. Extensive experiments validate the effectiveness of our methods, showcasing the potential for generating expressive, coordinated, and emotionally grounded human gestures.