M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, 2D Human Pose Estimation: New Benchmark and State of the Art Analysis, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.7, 2014.
DOI : 10.1109/CVPR.2014.471

L. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille, Attention to Scale: Scale-Aware Semantic Image Segmentation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.396

URL : http://arxiv.org/abs/1511.03339

W. Chen, Z. Fu, D. Yang, and J. Deng, Single-image depth perception in the wild, NIPS, issue.4, 2016.

Y. Du, Y. Wong, Y. Liu, F. Han, Y. Gui et al., Marker-Less 3D Human Motion Capture with Monocular Image Sequence and Height-Maps, 2016.
DOI : 10.1007/978-3-319-10584-0_23

D. Eigen and R. Fergus, Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.304

URL : http://arxiv.org/abs/1411.4734

D. Eigen, C. Puhrsch, and R. Fergus, Depth map prediction from a single image using a multi-scale deep network, NIPS, vol.4, p.5, 2014.

S. R. Fanello, C. Keskin, S. Izadi, P. Kohli, D. Kim et al., Learning to be a depth camera for close-range human capture and interaction, ACM Transactions on Graphics, vol.33, issue.4, 2014.
DOI : 10.1145/2601097.2601223

A. Gaidon, Q. Wang, Y. Cabon, and E. Vig, Virtual worlds as proxy for multi-object tracking analysis, 2016.
DOI : 10.1109/cvpr.2016.470

URL : http://arxiv.org/abs/1605.06457

M. F. Ghezelghieh, R. Kasturi, and S. Sarkar, Learning Camera Viewpoint Using CNN to Improve 3D Body Pose Estimation, 2016 Fourth International Conference on 3D Vision (3DV), p.3, 2016.
DOI : 10.1109/3DV.2016.75

URL : http://arxiv.org/abs/1609.05522

R. Green, Spherical harmonic lighting: The gritty details, Archives of the Game Developers Conference, 2003.

C. Ionescu, L. Fuxin, and C. Sminchisescu, Latent structured models for human pose estimation, 2011 International Conference on Computer Vision, p.5, 2011.
DOI : 10.1109/ICCV.2011.6126500

C. Ionescu, D. Papava, V. Olaru, C. Sminchisescu, and . Human3, Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.7, pp.1325-1339, 2005.
DOI : 10.1109/TPAMI.2013.248

F. Liu, C. Shen, and G. Lin, Deep convolutional neural fields for depth estimation from a single image, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299152

URL : http://arxiv.org/abs/1411.6387

M. Loper, N. Mahmood, J. Romero, G. Pons-moll, and M. J. Black, SMPL, ACM Transactions on Graphics, vol.34, issue.6, 2015.
DOI : 10.1145/2816795.2818013

M. M. Loper, N. Mahmood, and M. J. Black, MoSh, ACM Transactions on Graphics, vol.33, issue.6, 2014.
DOI : 10.1145/2661229.2661273

J. Marin, D. Vazquez, D. Geronimo, and A. M. Lopez, Learning appearance in virtual scenarios for pedestrian detection, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540218

R. Okada and S. Soatto, Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images, 2008.
DOI : 10.1007/978-3-540-88688-4_32

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.183.8996

G. Oliveira, A. Valada, C. Bollen, W. Burgard, and T. Brox, Deep learning for human part discovery in images, 2016 IEEE International Conference on Robotics and Automation (ICRA)
DOI : 10.1109/ICRA.2016.7487304

L. Pishchulin, A. Jain, M. Andriluka, T. Thormhlen, and B. Schiele, Articulated people detection and pose estimation: Reshaping the future, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248052

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.388.4003

G. Pons-moll, J. Romero, N. Mahmood, and M. J. Black, Dyna, ACM Transactions on Graphics, vol.34, issue.4, 2015.
DOI : 10.1145/2766993

W. Qiu, Generating human images and ground truth using computer graphics

H. Rahmani and A. Mian, Learning a non-linear knowledge transfer model for cross-view action recognition, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298860

H. Rahmani and A. Mian, 3D Action Recognition from Novel Viewpoints, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.167

H. Rhodin, C. Richardt, D. Casas, E. Insafutdinov, M. Shafiei et al., Ego- Cap: Egocentric marker-less motion capture with two fisheye cameras, SIGGRAPH Asia, 2016.

G. Rogez and C. Schmid, MoCap-guided data augmentation for 3D pose estimation in the wild, NIPS, issue.2 6, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01389486

J. Romero, M. Loper, and M. J. Black, FlowCap: 2D Human Pose from Optical Flow, 2015.
DOI : 10.1007/978-3-319-24947-6_34

B. Sapp and B. Taskar, MODEC: Multimodal Decomposable Models for Human Pose Estimation, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.471

J. Shotton, A. Fitzgibbon, A. Blake, A. Kipman, M. Finocchio et al., Real-time human pose recognition in parts from a single depth image, 2011.

H. Su, C. R. Qi, Y. Li, and L. J. Guibas, Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views, 2015 IEEE International Conference on Computer Vision (ICCV), p.2015
DOI : 10.1109/ICCV.2015.308

H. Yasin, U. Iqbal, B. Krger, A. Weber, and J. Gall, A dualsource approach for 3D pose estimation from a single image, 2016.
DOI : 10.1109/cvpr.2016.535

URL : http://arxiv.org/abs/1509.06720

F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao, LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop, 2015.

X. Zhou, M. Zhu, S. Leonardos, K. Derpanis, and K. Daniilidis, Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.537

URL : http://arxiv.org/abs/1511.09439