EmoDiffGes: Emotion-Aware Co-Speech Holistic Gesture Generation with Progressive Synergistic Diffusion
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Date
2025
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Journal ISSN
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
CCS Concepts: Computing methodologies → Computer graphics; Animation; Motion processing
@article{10.1111:cgf.70261,
journal = {Computer Graphics Forum},
title = {{EmoDiffGes: Emotion-Aware Co-Speech Holistic Gesture Generation with Progressive Synergistic Diffusion}},
author = {Li, Xinru and Lin, Jingzhong and Zhang, Bohao and Qi, Yuanyuan and Wang, Changbo and He, Gaoqi},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70261}
}