Abstract
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.
Abstract (translated)
视频预测是计算机视觉中一个基本任务，旨在使模型根据现有视频内容生成未来帧序列。这个任务在各种领域都得到了广泛应用。在本文中，我们全面调查了这个领域的 historical 和 contemporary 作品，涵盖了最常用的数据集和算法。我们的调查深入研究了视频预测在计算机视觉领域中的挑战和演变。我们提出了一个以视频预测算法随机性为基础的新分类器。这个分类器强调了从确定性预测方法向生成性预测方法的逐步转变，突出了方法的重大改进和思路的转变。
URL
https://arxiv.org/abs/2401.14718
