AI video generation transforms text, images, or existing video into dynamic visual sequences using deep learning models—primarily diffusion transformers, GANs, and autoregressive architectures trained on massive video datasets. Unlike traditional rendering, these models learn spatiotemporal patterns, motion dynamics, and scene coherence from real-world footage, enabling the synthesis of realistic, controllable videos at scale. The field has exploded since 2024, driven by breakthroughs in temporal consistency (maintaining coherent motion across frames), motion control (camera movements, object trajectories), and multimodal conditioning (text + audio + image inputs). Key challenges include balancing quality (FVD, VMAF scores) with inference speed, managing compute demands (latent-space compression via VAEs reduces cost 10–50×), and avoiding artifacts like flicker or morphing. Core workflow: prompt → tokenization → denoising/generation → post-processing. Understanding the interplay between architecture (transformer vs. GAN), training strategy (noise schedules, 3D CNNs), and conditioning mechanisms (ControlNet, optical flow) is essential—not all models prioritize the same tradeoffs, so matching technique to use case (cinematic realism vs. real-time previews vs. stylized animation) determines practical success.