The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
HOnnotate: A Method for 3D Annotation of Hand and Object Poses
Open accessAbstract
We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our motivation is the current lack of annotated real images for this problem, as estimating the 3D poses is challenging, mostly because of the mutual occlusions between the hand and the object. To tackle this challenge, we capture sequences with one or several RGB-D cameras and jointly optimize the 3D hand and object poses over all the frames simultaneously. This method allows us to automatically annotate each frame with accurate estimates of the poses, despite large mutual occlusions. With this method, we created HO-3D, the first markerless dataset of color images with 3D annotations for both the hand and object. This dataset is currently made of 77,558 frames, 68 sequences, 10 persons, and 10 objects. Using our dataset, we develop a single RGB image-based method to predict the hand pose when interacting with objects under severe occlusions and show it generalizes to objects not seen in the dataset.
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Citations
Cites
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., & Triebel, R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_43 · Cited by 564
Learning to recognize objects in egocentric activities
Fathi, A., Ren, X., & Rehg, J. M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995444 · Cited by 557
Accurate, 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
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
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
- Hampali, S., Rad, M., Oberweger, M., & Lepetit, V.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 3193-3203
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2994725031
- Cited by
- 422
- References
- 4
- FWCI
- 35.53
- Type
- conference paper
- Language
- en
- Open access
- green
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