The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
3D Human Pose Estimation = 2D Pose Estimation + Matching
Open accessAbstract
We explore 3D human pose estimation from a single RGB image. While many approaches try to directly predict 3D pose from image measurements, we explore a simple architecture that reasons through intermediate 2D pose predictions. Our approach is based on two key observations (1) Deep neural nets have revolutionized 2D pose estimation, producing accurate 2D predictions even for poses with self-occlusions (2) Big-datasets of 3D mocap data are now readily available, making it tempting to lift predicted 2D poses to 3D through simple memorization (e.g., nearest neighbors). The resulting architecture is straightforward to implement with off-the-shelf 2D pose estimation systems and 3D mocap libraries. Importantly, we demonstratethatsuchmethodsoutperformalmostallstate-of-theart 3D pose estimation systems, most of which directly try to regress 3D pose from 2D measurements.
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Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
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
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 3×
“We follow standard train/test protocol splits. but our analysis reveals that there has been inconsistent reporting in the literature, both in terms of test sets and evaluation criteria.”
BackgroundMethodInfluentialWei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.511 · Cited by 2,966
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.471 · Cited by 2,688
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,044
A Survey of Computer Vision-Based Human Motion Capture
Moeslund, T. B., & Granum, E. (2001) · Computer Vision and Image Understanding · doi:10.1006/cviu.2000.0897 · Cited by 1,768
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Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Tompson, J., Jain, A., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2984 · Cited by 915
Fast and globally convergent pose estimation from video images
Lu, C.-P., Hager, G. D., & Mjolsness, E. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.862199 · Cited by 882
Cited by
3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training
Pavllo, D., Feichtenhofer, C., Grangier, D., & Auli, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00794 · Cited by 1,297
Integral Human Pose Regression
Sun, X., Xiao, B., Wei, F., Liang, S., & Wei, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_33 · Cited by 874
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
2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Luvizon, D. C., Picard, D., & Tabia, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00539 · Cited by 555
Metadata
- Authors
- Chen, C.-H., & Ramanan, D.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5759-5767
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2583372902
- Cited by
- 566
- References
- 33
- FWCI
- 18.04
- Type
- conference paper
- Language
- en
- Open access
- green
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