Geometry-driven self-supervision for 3D human pose estimation
Author:
Funder
Institute for Information Communication Technology Planning and Evaluation
Publisher
Elsevier BV
Reference39 articles.
1. QMEDNet: a quaternion-based multi-order differential encoder–decoder model for 3D human motion prediction;Cao;Neural Networks,2022
2. Combined estimation of location and body pose in surveillance video;Chen,2011
3. Chen, X., Lin, K.-Y., Liu, W., Qian, C., & Lin, L. (2019). Weakly-supervised discovery of geometry-aware representation for 3d human pose estimation. In Conference on computer vision and pattern recognition (pp. 10895–10904).
4. Chen, C.-H., & Ramanan, D. (2017). 3d human pose estimation=2d pose estimation+ matching. In Conference on computer vision and pattern recognition (pp. 7035–7043).
5. Chen, C.-H., Tyagi, A., Agrawal, A., Drover, D., Mv, R., Stojanov, S., et al. (2019). Unsupervised 3d pose estimation with geometric self-supervision. In Conference on computer vision and pattern recognition (pp. 5714–5724).
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