Exploiting spatial-temporal relationships for 3D pose estimation via graph convolutional networks

• Main Authors: Cai, Yujun, Ge, Liuhao, Liu, Jun, Cai, Jianfei, Cham, Tat-Jen, Yuan, Junsong, Thalmann, Nadia Magnenat
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2019
• Subjects: 3D Pose Estimation; Graph Convolutional Neural Network (GCN); Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• Online Access: https://hdl.handle.net/10356/86102; http://hdl.handle.net/10220/49902
• Institution: Nanyang Technological University

Despite great progress in 3D pose estimation from single-view images or videos, it remains a challenging task due to the substantial depth ambiguity and severe selfocclusions. Motivated by the effectiveness of incorporating spatial dependencies and temporal consistencies to alleviate these issu...