The creation of lifelike speech-driven 3D facial animation requires a natural
and precise synchronization between audio input and facial expressions.
However, existing works still fail to render shapes with flexible head poses
and natural facial details (e.g., wrinkles). This limitation is mainly due to
two aspects: 1) Collecting training set with detailed 3D facial shapes is
highly expensive. This scarcity of detailed shape annotations hinders the
training of models with expressive facial animation. 2) Compared to mouth
movement, the head pose is much less correlated to speech content.
Consequently, concurrent modeling of both mouth movement and head pose yields
the lack of facial movement controllability. To address these challenges, we
introduce VividTalker, a new framework designed to facilitate speech-driven 3D
facial animation characterized by flexible head pose and natural facial
details. Specifically, we explicitly disentangle facial animation into head
pose and mouth movement and encode them separately into discrete latent spaces.
Then, these attributes are generated through an autoregressive process
leveraging a window-based Transformer architecture. To augment the richness of
3D facial animation, we construct a new 3D dataset with detailed shapes and
learn to synthesize facial details in line with speech content. Extensive
quantitative and qualitative experiments demonstrate that VividTalker
outperforms state-of-the-art methods, resulting in vivid and realistic
speech-driven 3D facial animation.

VividTalker 是一个新框架，旨在辅助具有灵活头部姿势和自然面部细节的以语音驱动的 3D 面部动画，通过将面部动画显式地分解为头部姿势和口腔运动，并将其单独编码为离散潜在空间，然后利用基于窗口的 Transformer 架构通过自回归过程生成这些特征。在与语音内容一致的情况下，使用新的 3D 数据集构建了具有详细形状的面部细节合成并学习。广泛的定量和定性实验证明，VividTalker 胜过了现有最先进的方法，实现了生动逼真的以语音驱动的 3D 面部动画。