Motion prediction is a classic problem in computer vision, which aims at forecasting future motion given the observed pose sequence. Various deep learning models have been proposed, achieving state-of-the-art performance on motion prediction. However, existing methods typically focus on modeling temporal dynamics in the pose space. Unfortunately, the complicated and high dimensionality nature of human motion brings inherent challenges for dynamic context capturing. Therefore, we move away from the conventional pose based representation and present a novel approach employing a phase space trajectory representation of individual joints. Moreover, current methods tend to only consider the dependencies between physically connected joints. In this paper, we introduce a novel convolutional neural model to effectively leverage explicit prior knowledge of motion anatomy, and simultaneously capture both spatial and temporal information of joint trajectory dynamics. We then propose a global optimization module that learns the implicit relationships between individual joint features. Empirically, our method is evaluated on large-scale 3D human motion benchmark datasets (i.e., Human3.6M, CMU MoCap). These results demonstrate that our method sets the new state-of-the-art on the benchmark datasets. Our code will be available at https://github.com/Pose-Group/TEID.

翻译：移动预测是计算机视觉的一个典型问题,其目的在于根据观察到的相形相貌序列预测未来运动。提出了各种深层次的学习模型,实现了运动预测的最先进的性能。然而,现有方法通常侧重于模拟表面空间的时间动态。不幸的是,人类运动的复杂和高度多维性质为动态环境捕捉带来了内在的挑战。因此,我们从传统的外形代表制出发,提出一种采用单个连接点的相位空间轨迹代表制的新办法。此外,目前的方法往往只考虑物理连接点之间的依赖性。在本文中,我们引入了一个新型的进化神经模型,以有效地利用对运动解剖学的明确先前知识,同时捕捉到联合轨迹动态的空间和时间信息。然后我们提出了一个全球优化模块,以了解各个联合点之间的隐含关系。我们的方法以大规模3D人类运动基准数据集(即人文3.6M、CMU MACP)来评估。这些结果显示,我们的方法在基准数据集上设置了新的状态。我们的代码将可在 httpTEID/comgroup上查阅。