Audio-driven talking head video generation enhances user engagement in human-computer interaction. However, current methods frequently produce videos with motion blur and lip jitter, primarily due to their reliance on implicit modeling of audio-facial motion correlations--an approach lacking explicit articulatory priors (i.e., anatomical guidance for speech-related facial movements). To overcome this limitation, we propose HM-Talker, a novel framework for generating high-fidelity, temporally coherent talking heads. HM-Talker leverages a hybrid motion representation combining both implicit and explicit motion cues. Explicit cues use Action Units (AUs), anatomically defined facial muscle movements, alongside implicit features to minimize phoneme-viseme misalignment. Specifically, our Cross-Modal Disentanglement Module (CMDM) extracts complementary implicit/explicit motion features while predicting AUs directly from audio input aligned to visual cues. To mitigate identity-dependent biases in explicit features and enhance cross-subject generalization, we introduce the Hybrid Motion Modeling Module (HMMM). This module dynamically merges randomly paired implicit/explicit features, enforcing identity-agnostic learning. Together, these components enable robust lip synchronization across diverse identities, advancing personalized talking head synthesis. Extensive experiments demonstrate HM-Talker's superiority over state-of-the-art methods in visual quality and lip-sync accuracy.

翻译：音频驱动的说话头视频生成增强了人机交互中的用户参与度。然而，现有方法常因依赖音频-面部运动关联的隐式建模——一种缺乏明确发音先验（即语音相关面部运动的解剖学指导）的方法——而产生运动模糊和唇部抖动的视频。为克服此局限，我们提出了HM-Talker，一种生成高保真、时序连贯说话头的新框架。HM-Talker利用结合隐式与显式运动线索的混合运动表征：显式线索采用解剖学定义的面部肌肉运动单元（AUs），与隐式特征协同以减少音素-视位错位。具体而言，我们的跨模态解缠模块（CMDM）从对齐视觉线索的音频输入中提取互补的隐式/显式运动特征并直接预测AUs。为降低显式特征中的身份依赖性偏差并增强跨主体泛化能力，我们引入了混合运动建模模块（HMMM）。该模块动态融合随机配对的隐式/显式特征，强制进行身份无关学习。这些组件共同实现了跨多样身份的鲁棒唇部同步，推动了个性化说话头合成的发展。大量实验证明，HM-Talker在视觉质量与唇部同步准确性上均优于当前最先进方法。