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

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

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...

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Bibliographic Details
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 Paper
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

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