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|a 10.1109/TPAMI.2021.3087695
|2 doi
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|a eng
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| 100 |
1 |
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|a Xu, Yuanlu
|e verfasserin
|4 aut
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| 245 |
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|a Monocular 3D Pose Estimation via Pose Grammar and Data Augmentation
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a Date Completed 16.09.2022
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a data augmentation algorithm to further improve model robustness against appearance variations and cross-view generalization ability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Wang, Wenguan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Tengyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Xiaobai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xie, Jianwen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Song-Chun
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 10 vom: 09. Okt., Seite 6327-6344
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|x 1939-3539
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|g volume:44
|g year:2022
|g number:10
|g day:09
|g month:10
|g pages:6327-6344
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|u http://dx.doi.org/10.1109/TPAMI.2021.3087695
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