Expressive Speech-driven Facial Animation with controllable emotions
This work addresses the need for more expressive and flexible facial animation in applications like virtual avatars or entertainment, though it is incremental by building on prior speech-driven animation techniques.
The paper tackled the problem of generating realistic facial animation from speech with controllable emotional expressions, achieving superior performance in both emotional expressiveness and lip synchronization compared to existing methods.
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.