Emotion Guided Speech-Driven Facial Animation

Sewhan Chun, Daegeun Choe, Shindong Kang, Shounan An, Youngbak Jo, Insoo Oh

Abstract: The modern deep neural network has allowed an applicable level of speech-driven facial animation, simulating natural and precise 3D animation from speech data. Regardless, many of the works show weakness in drastic emotional expression and flexibility of the animation. In this work, we introduce emotion guided speech-driven facial animation, simultaneously proceeding with classification and regression from the speech data to generate a controllable level of evident emotional expression on facial animation. Performance using our method shows reasonable expressiveness of facial emotion with controllable flexibility. Extensive experiments indicate that our method generates more expressive facial animation with controllable flexibility compared to previous approaches.