The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
3D Hand Shape and Pose Estimation From a Single RGB Image
Open accessAbstract
This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image. Most current methods in 3D hand analysis from monocular RGB images only focus on estimating the 3D locations of hand keypoints, which cannot fully express the 3D shape of hand. In contrast, we propose a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose. To train networks with full supervision, we create a large-scale synthetic dataset containing both ground truth 3D meshes and 3D poses. When fine-tuning the networks on real-world datasets without 3D ground truth, we propose a weakly-supervised approach by leveraging the depth map as a weak supervision in training. Through extensive evaluations on our proposed new datasets and two public datasets, we show that our proposed method can produce accurate and reasonable 3D hand mesh, and can achieve superior 3D hand pose estimation accuracy when compared with state-of-the-art methods.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,613
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
End-to-End Recovery of Human Shape and Pose
Kanazawa, A., Black, M. J., Jacobs, D. W., & Malik, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00744 · Cited by 1,870
Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_34 · Cited by 1,498
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., & Jiang, Y.-G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_4 · Cited by 1,306
Cited by
FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images
Zimmermann, C., Ceylan, D., Yang, J., Russell, B., Argus, M. J., & Brox, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00090 · Cited by 445
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
Chao, Y.-W., Yang, W., Xiang, Y., Molchanov, P., Handa, A., Tremblay, J., Narang, Y. S., Van Wyk, K., Iqbal, U., Birchfield, S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00893 · Cited by 285
Tashakori, A., Jiang, Z., Servati, A., Soltanian, S., Narayana, H., Le, K., Nakayama, C., Yang, C.-L., Wang, Z. J., Eng, J. J., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00780-9 · Cited by 137
Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction
Lee, G.-H., & Lee, S.-W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00614 · Cited by 70
Metadata
- Authors
- Ge, L., Ren, Z., Li, Y., Xue, Z., Wang, Y., Cai, J., & Yuan, J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 10825-10834
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964093990
- Cited by
- 458
- References
- 57
- FWCI
- 54.10
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ge, L., Ren, Z., Li, Y., Xue, Z., Wang, Y., Cai, J., & Yuan, J. (2019). 3D Hand Shape and Pose Estimation From a Single RGB Image. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.01109
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