TY - GEN
T1 - JointFusionNet
T2 - 31st International Conference on Artificial Neural Networks, ICANN 2022
AU - Yuan, Zhiwei
AU - Yan, Yaping
AU - Du, Songlin
AU - Ikenaga, Takeshi
N1 - Funding Information:
Acknowledgment. This work was jointly supported by the National Natural Science Foundation of China under grant 62001110, the Natural Science Foundation of Jiangsu Province under grant BK20200353, and the “Zhishan Young Scholar” Program of Southeast University.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 3D human pose estimation plays important roles in various human-machine interactive applications, but how to efficiently utilize the joint structural global and local features of human pose in deep-learning-based methods has always been a challenge. In this paper, we propose a parallel structural global and local joint features fusion network based on inspiring observation pattern of human pose. To be specific, it is observed that there are common similar global features and local features in human pose cross actions. Therefore, we design global-local capture modules separately to capture features and finally fuse them. The proposed parallel global and local joint features fusion network, entitled JointFusionNet, significantly improve state-of-the-art models on both intra-scenario H36M and cross-scenario 3DPW datasets and lead to appreciable improvements in poses with more similar local features. Notably, it yields an overall improvement of 3.4 mm in MPJPE (relative 6.8 % improvement) over the previous best feature fusion based method [22] on H36M dataset in 3D human pose estimation.
AB - 3D human pose estimation plays important roles in various human-machine interactive applications, but how to efficiently utilize the joint structural global and local features of human pose in deep-learning-based methods has always been a challenge. In this paper, we propose a parallel structural global and local joint features fusion network based on inspiring observation pattern of human pose. To be specific, it is observed that there are common similar global features and local features in human pose cross actions. Therefore, we design global-local capture modules separately to capture features and finally fuse them. The proposed parallel global and local joint features fusion network, entitled JointFusionNet, significantly improve state-of-the-art models on both intra-scenario H36M and cross-scenario 3DPW datasets and lead to appreciable improvements in poses with more similar local features. Notably, it yields an overall improvement of 3.4 mm in MPJPE (relative 6.8 % improvement) over the previous best feature fusion based method [22] on H36M dataset in 3D human pose estimation.
KW - 3D human pose estimation
KW - Feature fusion
KW - Human structural joint features
UR - http://www.scopus.com/inward/record.url?scp=85138747143&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-15937-4_10
DO - 10.1007/978-3-031-15937-4_10
M3 - Conference contribution
AN - SCOPUS:85138747143
SN - 9783031159367
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 125
BT - Artificial Neural Networks and Machine Learning - ICANN 2022 - 31st International Conference on Artificial Neural Networks, Proceedings
A2 - Pimenidis, Elias
A2 - Aydin, Mehmet
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
A2 - Papaleonidas, Antonios
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 September 2022 through 9 September 2022
ER -