From Sora What We Can See: A Survey of Text-to-Video Generation
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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.
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Cited by 2 Pith papers
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MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation
MAVEN is a multi-agent prompt refinement framework that improves cultural fidelity in text-to-video generation, demonstrated on a new benchmark of 243 prompts and 972 videos across Chinese, American, and Romanian cultures.
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MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation
MAVEN introduces a multi-agent system for refining prompts in multicultural text-to-video generation and releases a benchmark of 243 prompts and 972 videos showing improved cultural relevance via parallel agent specia...
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