In the field of computer graphics and animation, the challenge of generating lifelike and expressive talking face animations has historically necessitated extensive 3D data and complex facial motion capture systems. However, this project presents an innovative approach to tackle this challenge, with the primary goal of producing realistic 3D motion coefficients for stylized talking face animations driven by a single reference image synchronized with audio input. Leveraging state-of-the-art deep learning techniques, including generative models, image-to-image translation networks, and audio processing methods, the methodology bridges the gap between static images and dynamic, emotionally rich facial animations. The ultimate aim is to synthesize talking face animations that exhibit seamless lip synchronization and natural eye blinking, thereby achieving an exceptional degree of realism and expressiveness, revolutionizing the realm of computer-generated character interactions.