Generating talking avatar driven by audio remains a significant challenge. Existing methods typically require high computational costs and often lack sufficient facial detail and realism, making them unsuitable for applications that demand high real-time performance and visual quality. Additionally, while some methods can synchronize lip movement, they still face issues with consistency between facial expressions and upper body movement, particularly during silent periods. In this paper, we introduce SyncAnimation, the first NeRF-based method that achieves audio-driven, stable, and real-time generation of speaking avatar by combining generalized audio-to-pose matching and audio-to-expression synchronization. By integrating AudioPose Syncer and AudioEmotion Syncer, SyncAnimation achieves high-precision poses and expression generation, progressively producing audio-synchronized upper body, head, and lip shapes. Furthermore, the High-Synchronization Human Renderer ensures seamless integration of the head and upper body, and achieves audio-sync lip. The project page can be found at https://syncanimation.github.io.
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Yujian Liu
Shanghai University of Medicine and Health Sciences
Shidang Xu
Sun Yat-sen University
Jing Guo
Beijing Institute of Technology
South China University of Technology
Beijing Institute of Technology
Beijing University of Posts and Telecommunications
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Liu et al. (Mon,) studied this question.
synapsesocial.com/papers/68d469d631b076d99fa670ae — DOI: https://doi.org/10.24963/ijcai.2025/185
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