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The creation of lifelike speech-driven 3D facial animation requires a naturaland precise synchronization between audio input and facial expressions.However, existing works still fail to render shapes with flexible head posesand natural facial details (e.g., wrinkles). This limitation is mainly due totwo aspects: 1) Collecting training set with detailed 3D facial shapes ishighly expensive. This scarcity of detailed shape annotations hinders thetraining of models with expressive facial animation. 2) Compared to mouthmovement, the head pose is much less correlated to speech content.Consequently, concurrent modeling of both mouth movement and head pose yieldsthe lack of facial movement controllability. To address these challenges, weintroduce VividTalker, a new framework designed to facilitate speech-driven 3Dfacial animation characterized by flexible head pose and natural facialdetails. Specifically, we explicitly disentangle facial animation into headpose and mouth movement and encode them separately into discrete latent spaces.Then, these attributes are generated through an autoregressive processleveraging a window-based Transformer architecture. To augment the richness of3D facial animation, we construct a new 3D dataset with detailed shapes andlearn to synthesize facial details in line with speech content. Extensivequantitative and qualitative experiments demonstrate that VividTalkeroutperforms state-of-the-art methods, resulting in vivid and realisticspeech-driven 3D facial animation.

Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape