The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Hand Keypoint Detection in Single Images Using Multiview Bootstrapping
Open accessAbstract
We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked as outliers. Finally, the reprojected triangulations are used as new labeled training data to improve the detector. We repeat this process, generating more labeled data in each iteration. We derive a result analytically relating the minimum number of views to achieve target true and false positive rates for a given detector. The method is used to train a hand keypoint detector for single images. The resulting keypoint detector runs in realtime on RGB images and has accuracy comparable to methods that use depth sensors. The single view detector, triangulated over multiple views, enables 3D markerless hand motion capture with complex object interactions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
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
Self-citationAccurate, Robust, and Flexible Real-time Hand Tracking
Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Leichter, I., Vinnikov, A., Wei, Y., et al. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702179 · Cited by 431
Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture
Tang, D., Chang, H. J., Tejani, A., & Kim, T.-K. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.490 · Cited by 398
Cited by
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536
Self-citationExpressive Body Capture: 3D Hands, Face, and Body From a Single Image
Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., & Black, M. J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01123 · Cited by 1,781
Learning the signatures of the human grasp using a scalable tactile glove
Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., & Matusik, W. (2019) · Nature · doi:10.1038/s41586-019-1234-z · Cited by 1,162
Romero (2017) · doi:10.1145/3130800.3130883 · Cited by 1,056
Probabilistic Regression for Visual Tracking
Danelljan, M., Van Gool, L., & Timofte, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00721 · Cited by 798
Chan, C., Ginosar, S., Zhou, T., & Efros, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00603 · Cited by 737
Deep learning tools for the measurement of animal behavior in neuroscience
Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.008 · Cited by 450
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182
Günel, S., Rhodin, H., Morales, D., Campagnolo, J., Ramdya, P., & Fua, P. (2019) · eLife · doi:10.7554/elife.48571 · Cited by 167
Speaker Inconsistency Detection in Tampered Video
Korshunov, P., & Marcel, S. (2018) · 2018 26th European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553270 · Cited by 97
SpaceNet MVOI: A Multi-View Overhead Imagery Dataset
Weir, N., Lindenbaum, D., Bastidas, A., Etten, A., Kumar, V., Mcpherson, S., Shermeyer, J., & Tang, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00108 · Cited by 95
Yang, J., Wang, K., Peng, X., & Qiao, Y. (2018) · Proceedings of the 20th ACM International Conference on Multimodal Interaction · doi:10.1145/3242969.3264981 · Cited by 79
Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge
George, A., Ravindran, A., Mendieta, M., & Tabkhi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3055775 · Cited by 73
Emotion Recognition from EEG and Facial Expressions: a Multimodal Approach
Chaparro, V., Gomez, A., Salgado, A., Quintero, O. L., Lopez, N., & Villa, L. F. (2018) · 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512407 · Cited by 27
An Infrared Thermography Model Enabling Remote Body Temperature Screening Up to 10 Meters
Chin, J. W., Long Wong, K., Chan, T. T., Suhartono, K., & So, R. H. Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00429 · Cited by 16
Autoencoding sensory substitution
Tóth, V., & Parkkonen, L. (2019) · Unpublished · doi:10.13140/rg.2.2.10576.87048 · Cited by 7
Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving
Guntz, T., Balzarini, R., Vaufreydaz, D., & Crowley, J. L. (2017) · arXiv · doi:10.48550/arxiv.1710.04486 · Cited by 4
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3
Metadata
- Authors
- Simon, T., Joo, H., Matthews, I., & Sheikh, Y.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4645-4653
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2609211631
- Cited by
- 1,119
- References
- 4
- FWCI
- 93.02
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
