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）。

MMVP: 基于动态矩阵的视频预测

MMVP: Motion-Matrix-based Video Prediction

This work targets human action recognition in video. While recent methods
typically represent actions by statistics of local video features, here we
argue for the importance of a representation derived from human pose. To this
end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN)
for action recognition. The descriptor aggregates motion and appearance
information along tracks of human body parts. We investigate different schemes
of temporal aggregation and experiment with P-CNN features obtained both for
automatically estimated and manually annotated human poses. We evaluate our
method on the recent and challenging JHMDB and MPII Cooking datasets. For both
datasets our method shows consistent improvement over the state of the art.

本研究提出了一种基于人体姿势的卷积神经网络描述子（P-CNN），该描述子通过人类身体部位的轨迹聚合运动和外观信息，可以显著提高在 JHMDB 和 MPII Cooking 数据集上进行的人体动作识别的最新表现。