The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Learning 3D Human Dynamics From Video
Open accessAbstract
From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video can be found at https://akanazawa.github.io/human_dynamics/.
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Cites
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“The final output of the temporal encoder has the same feature dimension as φ .”
MethodAn Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
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End-to-End Recovery of Human Shape and Pose
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Self-citationKeep 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
Self-citationA Simple Yet Effective Baseline for 3d Human Pose Estimation
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Cited by
Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop
Kolotouros, N., Pavlakos, G., Black, M., & Daniilidis, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00234 · Cited by 1,039
PARE: Part Attention Regressor for 3D Human Body Estimation
Kocabas, M., Huang, C.-H. P., Hilliges, O., & Black, M. J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01094 · Cited by 417
MotionBERT: A Unified Perspective on Learning Human Motion Representations
Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., & Wang, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01385 · Cited by 348
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., & Sun, Z. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01125 · Cited by 341
HuMoR: 3D Human Motion Model for Robust Pose Estimation
Rempe, D., Birdal, T., Hertzmann, A., Yang, J., Sridhar, S., & Guibas, L. J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01129 · Cited by 304
Metadata
- Authors
- Kanazawa, A., Zhang, J. Y., Felsen, P., & Malik, J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5607-5616
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2975420824
- Cited by
- 531
- References
- 60
- FWCI
- 23.78
- Type
- conference paper
- Language
- en
- Open access
- green
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