计算机科学
人工智能
杠杆(统计)
弹道
水准点(测量)
维数之咒
运动(物理)
运动捕捉
背景(考古学)
卷积神经网络
特征学习
代表(政治)
机器学习
计算机视觉
生物
政治
物理
古生物学
政治学
大地测量学
法学
地理
天文
作者
Pengxiang Su,Zhenguang Liu,Shuang Wu,Lei Zhu,Yifang Yin,Xuanjing Shen
标识
DOI:10.1145/3474085.3475237
摘要
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 is released at https://github.com/Pose-Group/TEID.
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