The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
3D Human Pose Estimation in the Wild by Adversarial Learning
Open accessAbstract
Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DCNNs). Despite their success on large-scale datasets collected in the constrained lab environment, it is difficult to obtain the 3D pose annotations for in-the-wild images. Therefore, 3D human pose estimation in the wild is still a challenge. In this paper, we propose an adversarial learning framework, which distills the 3D human pose structures learned from the fully annotated dataset to in-the-wild images with only 2D pose annotations. Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground-truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild. We also observe that a carefully designed information source for the discriminator is essential to boost the performance. Thus, we design a geometric descriptor, which computes the pairwise relative locations and distances between body joints, as a new information source for the discriminator. The efficacy of our adversarial learning framework with the new geometric descriptor has been demonstrated through extensive experiments on widely used public benchmarks. Our approach significantly improves the performance compared with previous state-of-the-art approaches.
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Cites
Unpaired image-to-image translation using cycle-consistent adversarial networks
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Image-to-Image Translation with Conditional Adversarial Networks
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539
Stacked Hourglass Networks for Human Pose Estimation
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Adversarial Discriminative Domain Adaptation
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DeepPose: Human Pose Estimation via Deep Neural Networks
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Cited by
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2987975 · Cited by 1,404
Through-Wall Human Pose Estimation Using Radio Signals
Zhao, M., Li, T., Alsheikh, M. A., Tian, Y., Zhao, H., Torralba, A., & Katabi, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00768 · Cited by 658
Semantic Graph Convolutional Networks for 3D Human Pose Regression
Zhao, L., Peng, X., Tian, Y., Kapadia, M., & Metaxas, D. N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00354 · Cited by 522
Monocular human pose estimation: A survey of deep learning-based methods
Chen, Y., Tian, Y., & He, M. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102897 · Cited by 441
AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild
Joska, D., Clark, L., Muramatsu, N., Jericevich, R., Nicolls, F., Mathis, A., Mathis, M. W., & Patel, A. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561338 · Cited by 66
Metadata
- Authors
- Yang, W., Ouyang, W., Wang, X., Ren, J., Li, H., & Wang, X.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5255-5264
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2795089319
- Cited by
- 406
- References
- 58
- FWCI
- 18.31
- Type
- conference paper
- Language
- en
- Open access
- green
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