A Survey on Long-Video Storytelling Generation: Architectures,
  Consistency, and Cinematic Quality
Authors: name: Mohamed Elmoghany, name: Ryan Rossi, name: Seunghyun Yoon, name: Subhojyoti Mukherjee, name: Eslam Bakr, name: Puneet Mathur, name: Gang Wu, name: Viet Dac Lai, name: Nedim Lipka, name: Ruiyi Zhang, name: Varun Manjunatha, name: Chien Nguyen, name: Daksh Dangi, name: Abel Salinas, name: Mohammad Taesiri, name: Hongjie Chen, name: Xiaolei Huang, name: Joe Barrow, name: Nesreen Ahmed, name: Hoda Eldardiry, name: Namyong Park, name: Yu Wang, name: Jaemin Cho, name: Anh Totti Nguyen, name: Zhengzhong Tu, name: Thien Nguyen, name: Dinesh Manocha, name: Mohamed Elhoseiny, name: Franck Dernoncourt

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.