The field of computer vision is currently undergoing a pivotal transformation, shifting its focus from discriminative to generative tasks. Over the past two decades, the discipline was primarily defined by the discriminative imperative, which sought to enable machines to perceive, classify, and segment the visual world. However, catalyzed by the development of the Diffusion Transformer (DiT), the years 2024 and 2025 marked a Generative Turn, where the benchmark of artificial visual intelligence has evolved from mere classification to controllable simulation. The ability to generate high-fidelity, physically consistent video has led to the development of advanced generative models capable of representing underlying physical dynamics and environmental causality through large-scale data and computation. This survey provides a comprehensive analysis of the recent emergence of high-fidelity video generation. It traces the evolution from the era of feature engineering to the current Diffusion Transformers (DiTs) based generation era, summarizes the present state of video generation and the technical advancements driving this period, and offers a guide detailing the architectures, data selection, and training methodologies essential for high-fidelity video generation.