Demonstrates improved gesture generation in multilingual settings, suggesting better interaction.
Co-speech gesture generation is crucial for enhancing human-computer interaction. Most existing research focuses on English, overlooking multilingual speech and the ability to generalize across languages. Additionally, these studies often rely on pre-trained audio feature extraction models, insufficient for non-English languages. To address these issues, we introduce MDG, a novel diffusion model-based method for speech-driven gesture generation using a multilingual dataset. Our approach incorporates Language ID to distinguish between languages and utilizes low-level audio features to improve multilingual generalization. Extensive experiments demonstrate that MDG significantly outperforms baseline models in multilingual co-speech gesture generation. For more details, please visit our demo page1.
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Yang et al. (2024) studied this question.
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