Summary of From Sora What We Can See: a Survey Of Text-to-video Generation, by Rui Sun et al.
From Sora What We Can See: A Survey of Text-to-Video Generation
by Rui Sun, Yumin Zhang, Tejal Shah, Jiahao Sun, Shuoying Zhang, Wenqi Li, Haoran Duan, Bo Wei, Rajiv Ranjan
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI research paper explores the capabilities of Sora, a text-to-video generation model developed by OpenAI, which demonstrates minute-level world-simulative abilities. The study aims to understand what can be learned from Sora’s successes and limitations, focusing on its applications in text-to-video generation. The abstract presents an overview of the literature review, categorizing algorithms into evolutionary generators, excellent pursuit, and realistic panorama dimensions, as well as detailing widely used datasets and metrics. The paper identifies challenges and open problems in this domain and proposes future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sora is a powerful AI model that can generate videos from text descriptions. Researchers are trying to understand what we can learn from Sora’s abilities and limitations. This study looks at how Sora works and what it can do, as well as what it struggles with. The authors also review previous research in this area, grouping different techniques into categories like evolutionary generators, excellent pursuit, and realistic panorama. They also discuss the datasets and metrics used to measure success. Overall, the paper aims to help us understand where we are in developing AI and where we need to go next. |
