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From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future.
Inferring 3d structure with a statistical image-based shape model
K. Grauman, G. Shakhnarovich, and T. Darrell · 2003
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Recovering 3d human pose from monocular images
A. Agarwal and B. Triggs · 2006
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Combined discriminative and generative articulated pose and non-rigid shape estimation
L. Sigal, A. Balan, and M. J. Black · 2008
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Estimating human shape and pose from a single image
P. Guan, A. Weiss, A. O. Balan, and M. J. Black · 2009
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Parametric reshaping of human bodies in images
S. Zhou, H. Fu, L. Liu, D. Cohen-Or, and X. Han · 2010
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Articulated human detection with flexible mixtures of parts
Y. Yang and D. Ramanan · 2013
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From actemes to action: A strongly-supervised representation for detailed action understanding
W. Zhang, M. Zhu, and K. G. Derpanis · 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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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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Nrsfm using local rigidity
A. Rehan, A. Zaheer, I. Akhter, A. Saeed, M. H. Usmani, B. Mahmood, and S. Khan · 2014
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Recurrent network models for human dynamics
K. Fragkiadaki, S. Levine, P. Felsen, and J. Malik · 2015
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SMPL: A skinned multi-person linear model
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black · 2015
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Dense optical flow prediction from a static image
J. Walker, A. Gupta, and M. Hebert · 2015
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Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image
F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero, and M. J. Black · 2016
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Human pose estimation with iterative error feedback
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik · 2016
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Structural-rnn: Deep learning on spatio-temporal graphs
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena · 2016
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DeepCut: Joint subset partition and labeling for multi person pose estimation
L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. Gehler, and B. Schiele · 2016
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General automatic human shape and motion capture using volumetric contour cues
H. Rhodin, N. Robertini, D. Casas, C. Richardt, H.-P. Seidel, and C. Theobalt · 2016
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An uncertain future: Forecasting from static images using variational autoencoders
J. Walker, C. Doersch, A. Gupta, and M. Hebert · 2016
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3d reconstruction of human motion from monocular image sequences
B. Wandt, H. Ackermann, and B. Rosenhahn · 2016
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Single image 3d interpreter network
J. Wu, T. Xue, J. J. Lim, Y. Tian, J. B. Tenenbaum, A. Torralba, and W. T. Freeman · 2016
Cited alongside, same era.
Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
T. Xue, J. Wu, K. Bouman, and B. Freeman · 2016
Cited alongside, same era.
Sparseness meets deepness: 3d human pose estimation from monocular video
X. Zhou, M. Zhu, S. Leonardos, K. G. Derpanis, and K. Daniilidis · 2016
Cited alongside, same era.
Optical flow-based 3d human motion estimation from monocular video
T. Alldieck, M. Kassubeck, B. Wandt, B. Rosenhahn, and M. Magnor · 2017
Cited alongside, same era.
Deep representation learning for human motion prediction and classification
J. Bütepage, M. J. Black, D. Kragic, and H. Kjellström · 2017
Cited alongside, same era.
Learning 3d human pose from structure and motion
R. Dabral, A. Mundhada, U. Kusupati, S. Afaque, A. Sharma, and A. Jain · 2018
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From lifestyle vlogs to everyday interactions
D. F. Fouhey, W. Kuo, A. A. Efros, and J. Malik · 2018
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Im2flow: Motion hallucination from static images for action recognition
R. Gao, B. Xiong, and K. Grauman · 2018
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Detect-and-Track: Efficient Pose Estimation in Videos
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri, and D. Tran · 2018
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Reticam: Real-time human performance capture from monocular video, 2018
M. Habermann, W. Xu, M. Zollhoefer, G. Pons-Moll, and C. Theobalt · 2018
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Exploiting temporal information for 3d human pose estimation
M. R. I. Hossain and J. J. Little · 2018
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Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh · 2017
Cited alongside, same era.
Forecasting human dynamics from static images
Y.-W. Chao, J. Yang, B. L. Price, S. Cohen, and J. Deng · 2017
Cited alongside, same era.
Long short-term memory kalman filters: Recurrent neural estimators for pose regularization
H. Coskun, F. Achilles, R. S. DiPietro, N. Navab, and F. Tombari · 2017
Cited alongside, same era.
Unsupervised learning of disentangled representations from video
E. L. Denton et al · 2017
Cited alongside, same era.
Towards accurate marker-less human shape and pose estimation over time
Y. Huang, F. Bogo, C. Lassner, A. Kanazawa, P. V. Gehler, J. Romero, I. Akhter, and M. J. Black · 2017
Cited alongside, same era.
Unite the people: Closing the loop between 3d and 2d human representations
C. Lassner, J. Romero, M. Kiefel, F. Bogo, M. J. Black, and P. V. Gehler · 2017
Cited alongside, same era.
Recurrent 3d pose sequence machines
M. Lin, L. Lin, X. Liang, K. Wang, and H. Cheng · 2017
Cited alongside, same era.
Total capture: A 3d deformation model for tracking faces, hands, and bodies
H. Joo, T. Simon, and Y. Sheikh · 2018
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End-to-end recovery of human shape and pose
A. Kanazawa, M. J. Black, D. W. Jacobs, and J. Malik · 2018
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Flow-grounded spatial-temporal video prediction from still images
Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu, and M.-H. Yang · 2018
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Auto-conditioned recurrent networks for extended complex human motion synthesis
Z. Li, Y. Zhou, S. Xiao, C. He, Z. Huang, and H. Li · 2018
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Neural body fitting: Unifying deep learning and model-based human pose and shape estimation
M. Omran, C. Lassner, G. Pons-Moll, P. V. Gehler, and B. Schiele · 2018
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Learning to estimate 3D human pose and shape from a single color image
G. Pavlakos, L. Zhu, X. Zhou, and K. Daniilidis · 2018
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Sfv: Reinforcement learning of physical skills from videos
X. B. Peng, A. Kanazawa, J. Malik, P. Abbeel, and S. Levine · 2018
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Data distillation: Towards omni-supervised learning
I. Radosavovic, P. Dollár, R. Girshick, G. Gkioxari, and K. He · 2018
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Deep autoencoder for combined human pose estimation and body model upscaling
M. Trumble, A. Gilbert, A. Hilton, and J. Collomosse · 2018
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BodyNet: Volumetric inference of 3D human body shapes
G. Varol, D. Ceylan, B. Russell, J. Yang, E. Yumer, I. Laptev, and C. Schmid · 2018
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Neural kinematic networks for unsupervised motion retargetting
R. Villegas, J. Yang, D. Ceylan, and H. Lee · 2018
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Recovering accurate 3d human pose in the wild using imus and a moving camera
T. von Marcard, R. Henschel, M. Black, B. Rosenhahn, and G. Pons-Moll · 2018
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Monoperfcap: Human performance capture from monocular video
W. Xu, A. Chatterjee, M. Zollhöfer, H. Rhodin, D. Mehta, H.-P. Seidel, and C. Theobalt · 2018
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Monocular 3d pose and shape estimation of multiple people in natural scenes-the importance of multiple scene constraints
A. Zanfir, E. Marinoiu, and C. Sminchisescu · 2018
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Stable recurrent models
J. Miller and M. Hardt · 2019
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3d human pose estimation in video with temporal convolutions and semi-supervised training
D. Pavllo, C. Feichtenhofer, D. Grangier, and M. Auli · 2019
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