The field of AI-driven video generation is evolving rapidly, with remarkable advancements achieved over the past two years. These developments have markedly enhanced the ability to transform human imagination into realistic visual content across various domains. This survey provides a comprehensive review of contemporary research in video generation, delving into foundational principles -including diverse generation strategies and key generative frameworks- as well as state-of-the-art models encompassing video synthesis, editing, and enhancement techniques. We present a comparative analysis of critical components such as core technologies, video quality attributes, hardware prerequisites, openness, and essential evaluation metrics and datasets. The survey concludes by discussing open challenges and emerging directions in the field, such as the role of LLMs (Large Language Model) and VLMs (Vision-Language Model) in advancing the sophistication of video generation frameworks. We intend for this survey to serve as a useful resource for researchers and practitioners, offering a structured overview of recent advancements and a clear depiction of the current landscape in video generation research.