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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Hao-Shu Fang

Shanghai Jiao Tong University


Yuanlu Xu

University of California, Los Angeles


Wenguan Wang

Beijing Institute of Technology


Xiaobai Liu

San Diego State University


Song-Chun Zhu

University of California, Los Angeles


DOI:

10.1609/aaai.v32i1.12270


Abstract:

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation. Our model directly takes 2D pose as input and learns a generalized 2D-3D mapping function. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNN) 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 pose sample simulator to augment training samples in virtual camera views, which further improves our model generalizability. 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.

Topics: AAAI

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HOW TO CITE:

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation AAAI 2018, .

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu (2018). Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu. Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu. 2018. Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu. (2018) "Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu, "Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation", AAAI, p., 2018.

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu. "Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu. "Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Hao-Shu Fang||Yuanlu Xu||Wenguan Wang||Xiaobai Liu||Song-Chun Zhu. Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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