Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation

Audio-driven 3D face animation is crucial for live streaming and augmented reality, yet most existing methods focus on specific individuals with predefined speaking styles, limiting adaptability to varied styles. To address this, we introduce MetaFace, a novel methodology for speaking style adaptation based on meta-learning. MetaFace comprises three key components: the Robust Meta Initialization Stage (RMIS) for foundational style adaptation, the Dynamic Relation Mining Neural Process (DRMN) to connect observed and unobserved speaking styles, and a Low-rank Matrix Memory Reduction Approach to optimize model efficiency and style detail learning. These innovations enable MetaFace to significantly outperform existing baselines and set a new state-of-the-art, as demonstrated by our experimental results.