The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Structured Prediction of 3D Human Pose with Deep Neural Networks
Open accessAbstract
Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework, which involves a high computational cost at inference time. In this paper, we introduce a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and account for joint dependencies. We demonstrate that our approach outperforms state-of-the-art ones both in terms of structure preservation and prediction accuracy.
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Citations
Cites
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653 · cited 13×
“In effect, this hidden layer and the CNN-based representation of the image play the same role as the kernel embeddings in KDE-based approaches [5, 12, 14], thus allowing us to account for structure within a direct regression framework.”
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Cited by
VNect: real-time 3D human pose estimation with a single RGB camera
Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.-P., Xu, W., Casas, D., & Theobalt, C. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073596 · Cited by 1,113
Learning to Estimate 3D Human Pose and Shape from a Single Color Image
Pavlakos, G., Zhu, L., Zhou, X., & Daniilidis, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00055 · Cited by 607
Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach
Zhou, X., Huang, Q., Sun, X., Xue, X., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.51 · Cited by 599
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
3D Human Pose Estimation in the Wild by Adversarial Learning
Yang, W., Ouyang, W., Wang, X., Ren, J., Li, H., & Wang, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00551 · Cited by 406
Metadata
- Authors
- Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., & Fua, P.
- Venue
- Procedings of the British Machine Vision Conference 2016
- Publisher
- British Machine Vision Association
- Published
- 2016
- Pages
- 130.1-130.11
- OpenAlex
- W2404595106
- Cited by
- 236
- References
- 1
- FWCI
- 10.92
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., & Fua, P. (2016). Structured Prediction of 3D Human Pose with Deep Neural Networks. Procedings of the British Machine Vision Conference 2016. https://doi.org/10.5244/c.30.130
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