A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915386974273536 |
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| author | Ming, Ruibo Huang, Zhewei Wu, Jingwei Ju, Zhuoxuan Jiang, Daxin Hu, Jianming Peng, Lihui Zhou, Shuchang |
| author_facet | Ming, Ruibo Huang, Zhewei Wu, Jingwei Ju, Zhuoxuan Jiang, Daxin Hu, Jianming Peng, Lihui Zhou, Shuchang |
| contents | Future Frame Synthesis (FFS), the task of generating subsequent video frames from context, represents a core challenge in machine intelligence and a cornerstone for developing predictive world models. This survey provides a comprehensive analysis of the FFS landscape, charting its critical evolution from deterministic algorithms focused on pixel-level accuracy to modern generative paradigms that prioritize semantic coherence and dynamic plausibility. We introduce a novel taxonomy organized by algorithmic stochasticity, which not only categorizes existing methods but also reveals the fundamental drivers--advances in architectures, datasets, and computational scale--behind this paradigm shift. Critically, our analysis identifies a bifurcation in the field's trajectory: one path toward efficient, real-time prediction, and another toward large-scale, generative world simulation. By pinpointing key challenges and proposing concrete research questions for both frontiers, this survey serves as an essential guide for researchers aiming to advance the frontiers of visual dynamic modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14718 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches Ming, Ruibo Huang, Zhewei Wu, Jingwei Ju, Zhuoxuan Jiang, Daxin Hu, Jianming Peng, Lihui Zhou, Shuchang Computer Vision and Pattern Recognition Future Frame Synthesis (FFS), the task of generating subsequent video frames from context, represents a core challenge in machine intelligence and a cornerstone for developing predictive world models. This survey provides a comprehensive analysis of the FFS landscape, charting its critical evolution from deterministic algorithms focused on pixel-level accuracy to modern generative paradigms that prioritize semantic coherence and dynamic plausibility. We introduce a novel taxonomy organized by algorithmic stochasticity, which not only categorizes existing methods but also reveals the fundamental drivers--advances in architectures, datasets, and computational scale--behind this paradigm shift. Critically, our analysis identifies a bifurcation in the field's trajectory: one path toward efficient, real-time prediction, and another toward large-scale, generative world simulation. By pinpointing key challenges and proposing concrete research questions for both frontiers, this survey serves as an essential guide for researchers aiming to advance the frontiers of visual dynamic modeling. |
| title | A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.14718 |