Using musculoskeletal models to generate physically-consistent data for 3D human pose, kinematic, dynamic, and muscle estimation
Author:
Funder
Canada Research Chairs Program
Natural Sciences and Engineering Research Council of Canada
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11044-024-10021-5.pdf
Reference120 articles.
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3. Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image 3D human pose. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1263–1272. IEEE, Honolulu (2017). https://doi.org/10.1109/CVPR.2017.139
4. Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y.: Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2017, pp. 398–407. IEEE, Venice (2017). https://doi.org/10.1109/ICCV.2017.51
5. Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T.J., Yuan, J., Thalmann, N.M.: Exploiting spatial-temporal relationships for 3D pose estimation via graph convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019, pp. 2272–2281. IEEE, Seoul (2019). https://doi.org/10.1109/ICCV.2019.00236
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