Personalizing Causal Audio-Driven Facial Motion via Dynamic Multi-modal Retrieval

Audio-driven facial animation is essential for immersive digital interaction, yet existing frameworks struggle to reconcile real-time streaming with high-fidelity personalization. Current methods either rely on latency-inducing audio look-ahead, or ask users to record scripted calibration sequences to pre-encode static identity embeddings that fail to capture dynamic idiosyncrasies. We present an end-to-end framework for personalized audio-driven facial motion generation, supporting causal, zero-lookahead streaming. We introduce two key innovations: (1) a causal multi-resolution motion tokenizer that captures both global temporal context and high-frequency articulatory details, and (2) a ...

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