系统梳理视频生成中时空一致性的关键方法与挑战。
A Survey: Spatiotemporal Consistency in Video Generation
- 将视频生成视为高维时空分布的序列采样过程。
- 覆盖模型、表征、框架等多维度最新进展。
- 适合关注AIGC视频质量与连贯性研究者阅读。
视频生成旨在生成时间连贯的视觉帧序列，是人工智能生成内容（AIGC）的重要进展。相较于静态图像生成，视频生成面临独特挑战：不仅要求单帧高质量，还需保障整个时空序列的一致性。尽管近年来关于视频生成中时空一致性的研究增多，但聚焦此核心问题的系统性综述仍较稀缺。本文将视频生成任务视为从高维时空分布中进行序列采样，系统讨论时空一致性机制。涵盖生成模型、特征表示、生成框架、后处理技术、训练策略、基准数据集与评估指标等多个维度，重点分析各类方法在维持时空一致性方面的机制与效果。最后探讨该领域未来方向与潜在挑战，为推进视频生成技术提供参考。项目链接：https://github.com/Yin-Z-Y/A-Survey-Spatiotemporal-Consistency-in-Video-Generation。
原文摘要 · Abstract (English)
Video generation aims to produce temporally coherent sequences of visual frames, representing a pivotal advancement in Artificial Intelligence Generated Content (AIGC). Compared to static image generation, video generation poses unique challenges: it demands not only high-quality individual frames but also strong temporal coherence to ensure consistency throughout the spatiotemporal sequence. Although research addressing spatiotemporal consistency in video generation has increased in recent years, systematic reviews focusing on this core issue remain relatively scarce. To fill this gap, this paper views the video generation task as a sequential sampling process from a high-dimensional spatiotemporal distribution, and further discusses spatiotemporal consistency. We provide a systematic review of the latest advancements in the field. The content spans multiple dimensions including generation models, feature representations, generation frameworks, post-processing techniques, training strategies, benchmarks and evaluation metrics, with a particular focus on the mechanisms and effectiveness of various methods in maintaining spatiotemporal consistency. Finally, this paper explores future research directions and potential challenges in this field, aiming to provide valuable insights for advancing video generation technology. The project link is https://github.com/Yin-Z-Y/A-Survey-Spatiotemporal-Consistency-in-Video-Generation.
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