MAG: Multi-Modal Aligned Autoregressive Co-Speech Gesture Generation without Vector Quantization

• URL: http://arxiv.org/abs/2503.14040v1
• Date: Tue, 18 Mar 2025 09:02:02 GMT
• Title: MAG: Multi-Modal Aligned Autoregressive Co-Speech Gesture Generation without Vector Quantization
• Authors: Binjie Liu, Lina Liu, Sanyi Zhang, Songen Gu, Yihao Zhi, Tianyi Zhu, Lei Yang, Long Ye,
• Abstract summary: This work focuses on full-body co-speech gesture generation. Existing methods typically employ an autoregressive model accompanied by vector-quantized tokens for gesture generation.<n>We propose MAG, a novel multi-modal aligned framework for high-quality and diverse co-speech gesture synthesis without relying on discrete tokenization.
• Score: 8.605691647343065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work focuses on full-body co-speech gesture generation. Existing methods typically employ an autoregressive model accompanied by vector-quantized tokens for gesture generation, which results in information loss and compromises the realism of the generated gestures. To address this, inspired by the natural continuity of real-world human motion, we propose MAG, a novel multi-modal aligned framework for high-quality and diverse co-speech gesture synthesis without relying on discrete tokenization. Specifically, (1) we introduce a motion-text-audio-aligned variational autoencoder (MTA-VAE), which leverages pre-trained WavCaps' text and audio embeddings to enhance both semantic and rhythmic alignment with motion, ultimately producing more realistic gestures. (2) Building on this, we propose a multimodal masked autoregressive model (MMAG) that enables autoregressive modeling in continuous motion embeddings through diffusion without vector quantization. To further ensure multi-modal consistency, MMAG incorporates a hybrid granularity audio-text fusion block, which serves as conditioning for diffusion process. Extensive experiments on two benchmark datasets demonstrate that MAG achieves stateof-the-art performance both quantitatively and qualitatively, producing highly realistic and diverse co-speech gestures.The code will be released to facilitate future research.

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