  It is in high demand to generate facial animation with high realism, but it
remains a challenging task. Existing approaches of speech-driven facial
animation can produce satisfactory mouth movement and lip synchronization, but
show weakness in dramatic emotional expressions and flexibility in emotion
control. This paper presents a novel deep learning-based approach for
expressive facial animation generation from speech that can exhibit
wide-spectrum facial expressions with controllable emotion type and intensity.
We propose an emotion controller module to learn the relationship between the
emotion variations (e.g., types and intensity) and the corresponding facial
expression parameters. It enables emotion-controllable facial animation, where
the target expression can be continuously adjusted as desired. The qualitative
and quantitative evaluations show that the animation generated by our method is
rich in facial emotional expressiveness while retaining accurate lip movement,
outperforming other state-of-the-art methods.
