Learning progressive joint propagation for human motion prediction

• Main Authors: Cai, Yujun, Huang, Lin, Wang, Yiwei, Cham, Tat-Jen, Cai, Jianfei, Yuan, Junsong, Liu, Jun, Yang, Xu, Zhu, Yiheng, Shen, Xiaohui, Liu, Ding, Liu, Jing, Thalmann, Nadia Magnenat
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision; Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; 3D Motion Prediction; Transformer Network
• Online Access: https://hdl.handle.net/10356/144139
• Institution: Nanyang Technological University

Despite the great progress in human motion prediction, it remains a challenging task due to the complicated structural dynamics of human behaviors. In this paper, we address this problem in three aspects. First, to capture the long-range spatial correlations and temporal dependencies, we apply a tra...