Video prediction, a fundamental task in computer vision, aims to enable models to generate sequences of future frames based on existing video content. This task has garnered widespread application across various domains. In this paper, we comprehensively survey both historical and contemporary works in this field, encompassing the most widely used datasets and algorithms. Our survey scrutinizes the challenges and evolving landscape of video prediction within the realm of computer vision. We propose a novel taxonomy centered on the stochastic nature of video prediction algorithms. This taxonomy accentuates the gradual transition from deterministic to generative prediction methodologies, underlining significant advancements and shifts in approach.

翻译：视频预测是计算机视觉中的一项基础任务，旨在使模型能够基于现有视频内容生成未来帧序列。该任务已在多个领域得到广泛应用。本文系统梳理了该领域内历史及当代的研究工作，涵盖了最常用的数据集与算法。我们深入剖析了计算机视觉领域中视频预测面临的挑战及其演化趋势，并提出了一种以视频预测算法随机性为核心的新型分类体系。该分类体系着重强调了从确定性预测方法到生成式预测方法的渐进式转变，凸显了研究思路的重要进展与范式更迭。