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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 4: AAAI-21 Technical Tracks 4

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

February 1, 2023

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Authors

Chenxin Xu

Cooperative Medianet Innovation Center, Shanghai Jiao Tong University


Siheng Chen

Cooperative Medianet Innovation Center, Shanghai Jiao Tong University


Maosen Li

Cooperative Medianet Innovation Center, Shanghai Jiao Tong University


Ya Zhang

Cooperative Medianet Innovation Center, Shanghai Jiao Tong University


DOI:

10.1609/aaai.v35i4.16409


Abstract:

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.

Topics: AAAI

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HOW TO CITE:

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation Proceedings of the AAAI Conference on Artificial Intelligence (2021) 3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation AAAI 2021, 3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang (2021). Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang. Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang. 2021. Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation. "Proceedings of the AAAI Conference on Artificial Intelligence". 3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang. (2021) "Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation", Proceedings of the AAAI Conference on Artificial Intelligence, p.3013-3021

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang, "Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation", AAAI, p.3013-3021, 2021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang. "Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang. "Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 3013-3021.

Chenxin Xu||Siheng Chen||Maosen Li||Ya Zhang. Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation. AAAI[Internet]. 2021[cited 2023]; 3013-3021.


ISSN: 2374-3468


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