  Current talking face generation methods mainly focus on speech-lip
synchronization. However, insufficient investigation on the facial talking
style leads to a lifeless and monotonous avatar. Most previous works fail to
imitate expressive styles from arbitrary video prompts and ensure the
authenticity of the generated video. This paper proposes an unsupervised
variational style transfer model (VAST) to vivify the neutral photo-realistic
avatars. Our model consists of three key components: a style encoder that
extracts facial style representations from the given video prompts; a hybrid
facial expression decoder to model accurate speech-related movements; a
variational style enhancer that enhances the style space to be highly
expressive and meaningful. With our essential designs on facial style learning,
our model is able to flexibly capture the expressive facial style from
arbitrary video prompts and transfer it onto a personalized image renderer in a
zero-shot manner. Experimental results demonstrate the proposed approach
contributes to a more vivid talking avatar with higher authenticity and richer
expressiveness.
