A central challenge of video prediction lies where the system has to reason
the objects' future motions from image frames while simultaneously maintaining
the consistency of their appearances across frames. This work introduces an
end-to-end trainable two-stream video prediction framework, Motion-Matrix-based
Video Prediction (MMVP), to tackle this challenge. Unlike previous methods that
usually handle motion prediction and appearance maintenance within the same set
of modules, MMVP decouples motion and appearance information by constructing
appearance-agnostic motion matrices. The motion matrices represent the temporal
similarity of each and every pair of feature patches in the input frames, and
are the sole input of the motion prediction module in MMVP. This design
improves video prediction in both accuracy and efficiency, and reduces the
model size. Results of extensive experiments demonstrate that MMVP outperforms
state-of-the-art systems on public data sets by non-negligible large margins
(about 1 db in PSNR, UCF Sports) in significantly smaller model sizes (84% the
size or smaller). Please refer to
this https URL for the
official code and the datasets used in this paper.

该研究引入了一种端到端可训练的双流视频预测框架 —— 基于运动矩阵的视频预测（MMVP），旨在应对视频预测中对象未来运动的推理问题并保持其在帧之间的一致性。通过构建与外观无关的运动矩阵，MMVP 将运动和外观信息分离，从而提高了视频预测的准确性和效率，并减小了模型尺寸。大量实验证明，MMVP 在公共数据集上以小得多的模型尺寸（84% 或更小）显著优于现有系统（PSNR 上大约提高了 1db，UCF Sports）。