From Sora What We Can See: A Survey of Text-to-Video Generation
<|reference_start|>From Sora What We Can See: A Survey of Text-to-Video Generation: With impressive achievements made, artificial intelligence is on the path forward to artificial general intelligence. Sora, developed by OpenAI, which is capable of minute-level world-simulative abilities can be considered as a milestone on this developmental path. However, despite its notable successes, Sora still encounters various obstacles that need to be resolved. In this survey, we embark from the perspective of disassembling Sora in text-to-video generation, and conducting a comprehensive review of literature, trying to answer the question, \textit{From Sora What We Can See}. Specifically, after basic preliminaries regarding the general algorithms are introduced, the literature is categorized from three mutually perpendicular dimensions: evolutionary generators, excellent pursuit, and realistic panorama. Subsequently, the widely used datasets and metrics are organized in detail. Last but more importantly, we identify several challenges and open problems in this domain and propose potential future directions for research and development.<|reference_end|>
@article{sun2024from,
    title={From Sora What We Can See: A Survey of Text-to-Video Generation},
    author={Rui Sun, Yumin Zhang, Tejal Shah, Jiahao Sun, Shuoying Zhang, Wenqi
  Li, Haoran Duan, Bo Wei, Rajiv Ranjan},
    journal={arXiv preprint arXiv:2405.10674},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10674},
    primaryClass={cs.CV cs.AI}
}