A study on AI generated animated videos

The field of character animation has undergone a significant transformation with the advent of artificial intelligence (AI). Traditional animation techniques relied on manual frame-by-frame drawing and motion capture, but recent advancements in AI-driven methodologies have revolutionized the process, making it more efficient, realistic, and scalable. This study explores the evolution of AI techniques in character animation, focusing on deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models. These models have contributed to enhancing motion synthesis, expression generation, and overall realism in animated characters. Additionally, neural rendering and transformer-based architectures have further refined temporal consistency and controllability in AI-generated animation. This research presents a comparative analysis of different AI-driven animation methodologies, highlighting their strengths and limitations. The study also discusses future directions, including hybrid models and multi-modal learning, which promise further advancements in AI-powered character animation. Through this analysis, we aim to provide insights into the potential of AI in redefining the animation industry and establishing new standards for high-quality, automated character animation.