Video generation is a generative technique that uses artificial intelligence models to automatically create, edit, or complete video content. Its goal is to generate dynamic content with temporal continuity and visual consistency based on text descriptions, images, video clips, or other conditional information. This technology combines computer vision, generative models, multimodal learning, and video understanding, and is one of the important research directions in the field of AI content generation.

The development of video generation builds upon long-term research in multiple fields, including image generation, video prediction, and computer vision. Early methods primarily relied on rule-based animation generation, video stitching, and traditional visual modeling techniques, limiting their generation capabilities and content diversity. With the development of Generative Adversarial Networks (GANs), Diffusion Models, and large-scale pre-trained visual models, researchers began exploring the use of deep generative models to directly learn video data distributions. In 2022, Meta AI researchers published a paper... Make-A-Video: Text-to-Video Generation without Text-Video Data The paper proposes a text-to-video generation method that utilizes image-text data and video learning strategies to achieve text-driven video generation in the absence of large-scale text-video pairing data, becoming one of the important representative studies in the field of text-to-video generation.

Modern video generation systems typically rely on diffusion models, spatiotemporal modeling methods, and multimodal large model techniques to map text prompts, image conditions, or other control signals into continuous video content. Compared to traditional video production workflows, AI video generation can reduce some content creation costs and improve video generation and editing efficiency. Currently, this technology is widely used in scenarios such as film and television previews, advertising production, game content generation, educational demonstrations, virtual character creation, and digital media creation. However, challenges remain in long-term consistency, understanding of physical laws, detail control, and generation reliability.