Video Is Worth a Thousand Images: Exploring the Latest Trends in Long Video Generation
It addresses the problem of creating extended, coherent videos for applications in entertainment, education, and AI research, but is incremental as it synthesizes existing knowledge without introducing new methods.
This survey tackles the challenge of generating long videos, which remains limited to about one minute with current state-of-the-art systems like OpenAI's Sora, by exploring techniques such as GANs, diffusion models, and a divide-and-conquer approach to improve scalability and control.
An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significant progress in multimodal large language models (MLLMs), generating extended videos remains a formidable challenge. As of this writing, OpenAI's Sora, the current state-of-the-art system, is still limited to producing videos that are up to one minute in length. This limitation stems from the complexity of long video generation, which requires more than generative AI techniques for approximating density functions essential aspects such as planning, story development, and maintaining spatial and temporal consistency present additional hurdles. Integrating generative AI with a divide-and-conquer approach could improve scalability for longer videos while offering greater control. In this survey, we examine the current landscape of long video generation, covering foundational techniques like GANs and diffusion models, video generation strategies, large-scale training datasets, quality metrics for evaluating long videos, and future research areas to address the limitations of the existing video generation capabilities. We believe it would serve as a comprehensive foundation, offering extensive information to guide future advancements and research in the field of long video generation.