MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation
On the map: Diffusion models → Video generation
Abstract (arXiv)
Recent advancements in video generation models, like Stable Video Diffusion, show promising results, but primarily focus on short, single-scene videos. These models struggle with generating long videos that involve multiple scenes, coherent narratives, and consistent characters. Furthermore, there is no publicly available dataset tailored for the analysis, evaluation, and training of long video generation models. In this paper, we present MovieBench: A Hierarchical Movie-Level Dataset for Long Video Generation, which addresses these challenges by providing unique contributions: (1) movie-length videos featuring rich, coherent storylines and multi-scene narratives, (2) consistency of character appearance and audio across scenes, and (3) hierarchical data structure contains high-level movie information and detailed shot-level descriptions. Experiments demonstrate that MovieBench brings some new insights and challenges, such as maintaining character ID consistency across multiple scenes for various characters. The dataset will be public and continuously maintained, aiming to advance the field of long video generation. Data can be found at: https://weijiawu.github.io/MovieBench/.
Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 64 of its 78 references that are in the map. First posted on arXiv 22 Nov 2024.
Builds on
64 of 78 references.
Closest ancestors · 11
Same direction, newest first
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- 2023NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation22 Mar
- 2023Structure and Content-Guided Video Synthesis with Diffusion Models6 Feb
- 2022Video Diffusion Models7 Apr
Other references · 53
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Citing papers per year: 2025: 12; 2026: 24.
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