Learning Latent Representations of 3D Human Pose with Deep Neural Networks-Reference-Cited by-同舟云学术

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Learning Latent Representations of 3D Human Pose with Deep Neural Networks

Published:2018-01-31 Issue:12 Volume:126 Page:1326-1341
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Katircioglu Isinsu,Tekin Bugra,Salzmann Mathieu,Lepetit Vincent,Fua Pascal

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/article/10.1007/s11263-018-1066-6/fulltext.html

Reference66 articles.

1. Agarwal, A., & Triggs, B. (2004). 3D human pose from silhouettes by relevance vector regression. In CVPR.

2. Akhter, I., & Black, M. J. (2015). Pose-conditioned joint angle limits for 3D human pose reconstruction. In CVPR.

3. Bo, L., & Sminchisescu, C. (2010). Twin Gaussian processes for structured prediction. International Journal of Computer Vision, 87, 28–52.

4. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J. (2016). Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In ECCV.

5. Burenius, M., Sullivan, J., & Carlsson, S. (2013). 3D pictorial structures for multiple view articulated pose estimation. In CVPR.

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