Facial expressions reflect internal emotional states of a character or in response to social communications. Though much effort has been taken to generate realistic facial expressions, it still remains a challenging topic due to human being’s sensitivity to subtle facial movements. In this paper, we present a method for facial animation generation, which reflects true facial muscle movements with high fidelity. An intermediate model space is introduced to transfer captured static AU peak frames based on FACS to the conformed target face. And then dynamic parameters derived using a psychophysics method is integrated to generate facial animation, which is assumed to represent natural correlation of multiple AUs. Finally, the animation sequence in the intermediate model space is mapped to the target face to produce final animation.