The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Semantic Graph Convolutional Networks for 3D Human Pose Regression
Open accessAbstract
In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.
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Cites
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Cited by
Learning to Learn Single Domain Generalization
Qiao, F., Zhao, L., & Peng, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01257 · Cited by 455
Self-citationMixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video
Zhang, J., Tu, Z., Yang, J., Chen, Y., & Yuan, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01288 · Cited by 426
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Cheng, K., Zhang, Y., Cao, C., Shi, L., Cheng, J., & Lu, H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_32 · Cited by 384
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
Nie, Q., Liu, Z., & Liu, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58529-7_7 · Cited by 80
A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation
El Kaid, A., & Baïna, K. (2023) · Journal of Imaging · doi:10.3390/jimaging9120275 · Cited by 44
Metadata
- Authors
- Zhao, L., Peng, X., Tian, Y., Kapadia, M., & Metaxas, D. N.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3420-3430
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964318832
- Cited by
- 522
- References
- 51
- FWCI
- 17.63
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhao, L., Peng, X., Tian, Y., Kapadia, M., & Metaxas, D. N. (2019). Semantic Graph Convolutional Networks for 3D Human Pose Regression. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00354
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