A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality
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arXiv
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| Format: | Preprint |
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2025
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| author | Elmoghany, Mohamed Rossi, Ryan Yoon, Seunghyun Mukherjee, Subhojyoti Bakr, Eslam Mathur, Puneet Wu, Gang Lai, Viet Dac Lipka, Nedim Zhang, Ruiyi Manjunatha, Varun Nguyen, Chien Dangi, Daksh Salinas, Abel Taesiri, Mohammad Chen, Hongjie Huang, Xiaolei Barrow, Joe Ahmed, Nesreen Eldardiry, Hoda Park, Namyong Wang, Yu Cho, Jaemin Nguyen, Anh Totti Tu, Zhengzhong Nguyen, Thien Manocha, Dinesh Elhoseiny, Mohamed Dernoncourt, Franck |
| author_facet | Elmoghany, Mohamed Rossi, Ryan Yoon, Seunghyun Mukherjee, Subhojyoti Bakr, Eslam Mathur, Puneet Wu, Gang Lai, Viet Dac Lipka, Nedim Zhang, Ruiyi Manjunatha, Varun Nguyen, Chien Dangi, Daksh Salinas, Abel Taesiri, Mohammad Chen, Hongjie Huang, Xiaolei Barrow, Joe Ahmed, Nesreen Eldardiry, Hoda Park, Namyong Wang, Yu Cho, Jaemin Nguyen, Anh Totti Tu, Zhengzhong Nguyen, Thien Manocha, Dinesh Elhoseiny, Mohamed Dernoncourt, Franck |
| contents | Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-form videos". Furthermore, videos exceeding 16 seconds struggle to maintain consistent character appearances and scene layouts throughout the narrative. In particular, multi-subject long videos still fail to preserve character consistency and motion coherence. While some methods can generate videos up to 150 seconds long, they often suffer from frame redundancy and low temporal diversity. Recent work has attempted to produce long-form videos featuring multiple characters, narrative coherence, and high-fidelity detail. We comprehensively studied 32 papers on video generation to identify key architectural components and training strategies that consistently yield these qualities. We also construct a comprehensive novel taxonomy of existing methods and present comparative tables that categorize papers by their architectural designs and performance characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality Elmoghany, Mohamed Rossi, Ryan Yoon, Seunghyun Mukherjee, Subhojyoti Bakr, Eslam Mathur, Puneet Wu, Gang Lai, Viet Dac Lipka, Nedim Zhang, Ruiyi Manjunatha, Varun Nguyen, Chien Dangi, Daksh Salinas, Abel Taesiri, Mohammad Chen, Hongjie Huang, Xiaolei Barrow, Joe Ahmed, Nesreen Eldardiry, Hoda Park, Namyong Wang, Yu Cho, Jaemin Nguyen, Anh Totti Tu, Zhengzhong Nguyen, Thien Manocha, Dinesh Elhoseiny, Mohamed Dernoncourt, Franck Computer Vision and Pattern Recognition Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-form videos". Furthermore, videos exceeding 16 seconds struggle to maintain consistent character appearances and scene layouts throughout the narrative. In particular, multi-subject long videos still fail to preserve character consistency and motion coherence. While some methods can generate videos up to 150 seconds long, they often suffer from frame redundancy and low temporal diversity. Recent work has attempted to produce long-form videos featuring multiple characters, narrative coherence, and high-fidelity detail. We comprehensively studied 32 papers on video generation to identify key architectural components and training strategies that consistently yield these qualities. We also construct a comprehensive novel taxonomy of existing methods and present comparative tables that categorize papers by their architectural designs and performance characteristics. |
| title | A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.07202 |