The field of 3D animation is undergoing a transformative shift through deep learning. This paper presents a robust system that automates 3D facial animation from a static image using Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). With real-time deployment on cloud platforms, the approach bypasses the need for manual animation and motion-capture tools. Our system accepts a static face image and a driving video, mapping expressions and movements with high fidelity. Performance metrics such as SSIM (0.87), FID (23.4), and 23 FPS illustrate its capability. The proposed framework is adaptable to human, cartoon, and avatar faces, demonstrating a scalable path toward democratized 3D animation.