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Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications.
Civilian American and European Surface Anthropometry Resource (CAESAR), Final Report
K. Robinette, S. Blackwell, H. Daanen, M. Boehmer, S. Fleming, T. Brill, D. Hoeferlin, and D. Burnsides · 2002
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Spherical harmonic lighting: The gritty details
R. Green · 2003
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Learning joint top-down and bottom-up processes for 3D visual inference
C. Sminchisescu, A. Kanaujia, and D. Metaxas · 2006
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Relevant feature selection for human pose estimation and localization in cluttered images
R. Okada and S. Soatto · 2008
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Learning appearance in virtual scenarios for pedestrian detection
J. Marin, D. Vazquez, D. Geronimo, and A. M. Lopez · 2010
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Latent structured models for human pose estimation
C. Ionescu, L. Fuxin, and C. Sminchisescu · 2011
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Learning people detection models from few training samples
L. Pishchulin, A. Jain, C. Wojek, M. Andriluka, T. Thormählen, and B. Schiele · 2011
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Real-time human pose recognition in parts from a single depth image
J. Shotton, A. Fitzgibbon, , A. Blake, A. Kipman, M. Finocchio, R. Moore, and T. Sharp · 2011
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Articulated people detection and pose estimation: Reshaping the future
L. Pishchulin, A. Jain, M. Andriluka, T. Thormählen, and B. Schiele · 2012
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Multimodal decomposable models for human pose estimation
B. Sapp and B. Taskar · 2013
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2D human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Learning to be a depth camera for close-range human capture and interaction
S. R. Fanello, C. Keskin, S. Izadi, P. Kohli, D. Kim, D. Sweeney, A. Criminisi, J. Shotton, S. B. Kang, and T. Paek · 2014
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Iterated second-order label sensitive pooling for 3D human pose estimation
C. Ionescu, J. Carreira, and C. Sminchisescu · 2014
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Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu · 2014
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MoSh: Motion and shape capture from sparse markers
M. M. Loper, N. Mahmood, and M. J. Black · 2014
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FlowNet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
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Deep convolutional neural fields for depth estimation from a single image
F. Liu, C. Shen, and G. Lin · 2015
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SMPL: A skinned multi-person linear model
Synthesizing training images for boosting human 3D pose estimation
W. Chen, H. Wang, Y. Li, H. Su, Z. Wang, C. Tu, D. Lischinski, D. Cohen-Or, and B. Chen · 2016
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Marker-less 3D human motion capture with monocular image sequence and height-maps
Y. Du, Y. Wong, Y. Liu, F. Han, Y. Gui, Z. Wang, M. Kankanhalli, and W. Geng · 2016
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Virtual worlds as proxy for multi-object tracking analysis
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig · 2016
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Learning camera viewpoint using cnn to improve 3D body pose estimation
M. F. Ghezelghieh, R. Kasturi, and S. Sarkar · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Deep learning for human part discovery in images
G. Oliveira, A. Valada, C. Bollen, W. Burgard, and T. Brox · 2016
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M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black · 2015
Cited alongside, same era.
Learning deep object detectors from 3D models
X. Peng, B. Sun, K. Ali, and K. Saenko · 2015
Cited alongside, same era.
Dyna: A model of dynamic human shape in motion
G. Pons-Moll, J. Romero, N. Mahmood, and M. J. Black · 2015
Cited alongside, same era.
Learning a non-linear knowledge transfer model for cross-view action recognition
H. Rahmani and A. Mian · 2015
Cited alongside, same era.
FlowCap: 2D human pose from optical flow
J. Romero, M. Loper, and M. J. Black · 2015
Cited alongside, same era.
Render for CNN: Viewpoint estimation in images using CNNs trained with rendered 3D model views
H. Su, C. R. Qi, Y. Li, and L. J. Guibas · 2015
Cited alongside, same era.
LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
Cited alongside, same era.
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Generating human images and ground truth using computer graphics
W. Qiu · 2016
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3D action recognition from novel viewpoints
H. Rahmani and A. Mian · 2016
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EgoCap: Egocentric marker-less motion capture with two fisheye cameras
H. Rhodin, C. Richardt, D. Casas, E. Insafutdinov, M. Shafiei, H.-P. Seidel, B. Schiele, and C. Theobalt · 2016
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MoCap-guided data augmentation for 3D pose estimation in the wild
G. Rogez and C. Schmid · 2016
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Convolutional pose machines
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
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A dual-source approach for 3D pose estimation from a single image
H. Yasin, U. Iqbal, B. Krüger, A. Weber, and J. Gall · 2016
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Sparseness meets deepness: 3D human pose estimation from monocular video
X. Zhou, M. Zhu, S. Leonardos, K. Derpanis, and K. Daniilidis · 2016
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