Diffusion-Based Co-Speech Gesture Generation Using Joint Text and Audio Representation

• URL: http://arxiv.org/abs/2309.05455v1
• Date: Mon, 11 Sep 2023 13:51:06 GMT
• Title: Diffusion-Based Co-Speech Gesture Generation Using Joint Text and Audio Representation
• Authors: Anna Deichler, Shivam Mehta, Simon Alexanderson, Jonas Beskow
• Abstract summary: This paper describes a system developed for the GENEA (Generation and Evaluation of Non-verbal Behaviour for Embodied Agents) Challenge 2023. We propose a contrastive speech and motion pretraining (CSMP) module, which learns a joint embedding for speech and gesture. The output of the CSMP module is used as a conditioning signal in the diffusion-based gesture synthesis model.
• Score: 18.04996323708772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes a system developed for the GENEA (Generation and Evaluation of Non-verbal Behaviour for Embodied Agents) Challenge 2023. Our solution builds on an existing diffusion-based motion synthesis model. We propose a contrastive speech and motion pretraining (CSMP) module, which learns a joint embedding for speech and gesture with the aim to learn a semantic coupling between these modalities. The output of the CSMP module is used as a conditioning signal in the diffusion-based gesture synthesis model in order to achieve semantically-aware co-speech gesture generation. Our entry achieved highest human-likeness and highest speech appropriateness rating among the submitted entries. This indicates that our system is a promising approach to achieve human-like co-speech gestures in agents that carry semantic meaning.

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