Title:	3D POSE ESTIMATION BY GROUPED FEATURE FUSION AND MOTION AMPLITUDE ENCODING
Author(s):	Jihua Peng, Yanghong Zhou and P.Y. Mok
ISBN:	978-989-8704-42-9
Editors:	Yingcai Xiao, Ajith Abraham, Guo Chao Peng and Jörg Roth
Year:	2022
Edition:	Single
Keywords:	Human Pose Estimation, Feature Fusion, Motion Amplitude
Type:	Full Paper
First Page:	27
Last Page:	34
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	3D human pose estimation is challenging and converting it to a local pose estimation problem by dividing human body into different groups based on anatomical relationships can improve the accuracy of the resulting 3D pose estimation. Joint features of different groups are fused to predict complete pose information of entire body, in which a joint feature fusion scheme must be used for this purpose. However, the joint feature fusion adopted in existing methods has to learn a large number of parameters and is computational expensive. In this paper, we propose an optimized feature fusion (OFF) module, which requires fewer parameters and less calculations while ensures prediction accuracy. Moreover, we also propose a motion amplitude encoding (MAE) method to improve the prediction accuracy for small ranged movements. Experiments have shown that our method outperforms previous state-of-the-art results on Human3.6M dataset.
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