  Speech-driven 3D facial animation technology has been developed for years,
but its practical application still lacks expectations. The main challenges lie
in data limitations, lip alignment, and the naturalness of facial expressions.
Although lip alignment has seen many related studies, existing methods struggle
to synthesize natural and realistic expressions, resulting in a mechanical and
stiff appearance of facial animations. Even with some research extracting
emotional features from speech, the randomness of facial movements limits the
effective expression of emotions. To address this issue, this paper proposes a
method called CSTalk (Correlation Supervised) that models the correlations
among different regions of facial movements and supervises the training of the
generative model to generate realistic expressions that conform to human facial
motion patterns. To generate more intricate animations, we employ a rich set of
control parameters based on the metahuman character model and capture a dataset
for five different emotions. We train a generative network using an autoencoder
structure and input an emotion embedding vector to achieve the generation of
user-control expressions. Experimental results demonstrate that our method
outperforms existing state-of-the-art methods.
