LLM Gesticulator: Leveraging Large Language Models for Scalable and Controllable Co-Speech Gesture Synthesis

• URL: http://arxiv.org/abs/2410.10851v2
• Date: Tue, 22 Oct 2024 13:08:02 GMT
• Title: LLM Gesticulator: Leveraging Large Language Models for Scalable and Controllable Co-Speech Gesture Synthesis
• Authors: Haozhou Pang, Tianwei Ding, Lanshan He, Ming Tao, Lu Zhang, Qi Gan,
• Abstract summary: We present LLM Gesticulator, an audio-driven co-speech gesture generation framework. Our framework synthesizes full-body animations that are rhythmically aligned with the input audio while exhibiting natural movements and editability. Our method also exhibits strong controllability where the content, style of the generated gestures can be controlled by text prompt.
• Score: 4.762487293009696
• License:
• Abstract: In this work, we present LLM Gesticulator, an LLM-based audio-driven co-speech gesture generation framework that synthesizes full-body animations that are rhythmically aligned with the input audio while exhibiting natural movements and editability. Compared to previous work, our model demonstrates substantial scalability. As the size of the backbone LLM model increases, our framework shows proportional improvements in evaluation metrics (a.k.a. scaling law). Our method also exhibits strong controllability where the content, style of the generated gestures can be controlled by text prompt. To the best of our knowledge, LLM gesticulator is the first work that use LLM on the co-speech generation task. Evaluation with existing objective metrics and user studies indicate that our framework outperforms prior works.

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