Fetching the paper…
Reading the bibliography…
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.
mocap. cs. cmu. edu, 2003
C. MoCap · 2003
Earlier work this paper cites.
Data driven image models through continuous joint alignment
E. G. Learned-Miller · 2006
Earlier work this paper cites.
Unsupervised learning of object deformation models
I. Kokkinos and A. L. Yuille · 2007
Earlier work this paper cites.
Clustered pose and nonlinear appearance models for human pose estimation
S. Johnson and M. Everingham · 2010
Earlier work this paper cites.
Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion
L. Sigal, A. O. Balan, and M. J. Black · 2010
Earlier work this paper cites.
Learning people detection models from few training samples
L. Pishchulin, A. Jain, C. Wojek, M. Andriluka, T. Thormählen, and B. Schiele · 2011
Earlier work this paper cites.
Articulated people detection and pose estimation: Reshaping the future
L. Pishchulin, A. Jain, M. Andriluka, T. Thormählen, and B. Schiele · 2012
Earlier work this paper cites.
The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation
J. Taylor, J. Shotton, T. Sharp, and A. W. Fitzgibbon · 2012
Earlier work this paper cites.
Parsing clothing in fashion photographs
K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg · 2012
Earlier work this paper cites.
2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
Earlier work this paper cites.
Detect what you can: Detecting and representing objects using holistic models and body parts
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille · 2014
Earlier work this paper cites.
Body parts dependent joint regressors for human pose estimation in still images
M. Dantone, J. Gall, C. Leistner, and L. Van Gool · 2014
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Dense semantic correspondence where every pixel is a classifier
H. Bristow, J. Valmadre, and S. Lucey · 2015
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. J. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. S. Lempitsky · 2015
Cited alongside, same era.
Deeply-supervised nets
C. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu · 2015
Cited alongside, same era.
SMPL: A skinned multi-person linear model
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black · 2015
Cited alongside, same era.
Metric regression forests for correspondence estimation
G. Pons-Moll, J. Taylor, J. Shotton, A. Hertzmann, and A. Fitzgibbon · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Learning dense correspondence via 3d-guided cycle consistency
T. Zhou, P. Krähenbühl, M. Aubry, Q. Huang, and A. A. Efros · 2016
Later among the works it cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Later among the works it cites.
Weakly supervised manifold learning for dense semantic object correspondence
U. Gaur and B. S. Manjunath · 2017
Later among the works it cites.
Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing
K. Gong, X. Liang, X. Shen, and L. Lin · 2017
Later among the works it cites.
Densereg: Fully convolutional dense shape regression in-the-wild
R. A. Güler, G. Trigeorgis, E. Antonakos, P. Snape, S. Zafeiriou, and I. Kokkinos · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2015
Cited alongside, same era.
Keep it SMPL: automatic estimation of 3d human pose and shape from a single image
F. Bogo, A. Kanazawa, C. Lassner, P. V. Gehler, J. Romero, and M. J. Black · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Learning camera viewpoint using cnn to improve 3d body pose estimation
M. F. Ghezelghieh, R. Kasturi, and S. Sarkar · 2016
Cited alongside, same era.
Deepercut: A deeper, stronger, and faster multi-person pose estimation model
E. Insafutdinov, L. Pishchulin, M. Andriluka, and B. Schiele · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollar, and R. Girshick · 2017
Later among the works it cites.
End-to-end recovery of human shape and pose
A. Kanazawa, M. J. Black, D. W. Jacobs, and Jitendra · 2017
Later among the works it cites.
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
Later among the works it cites.
Feature pyramid networks for object detection
T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Later among the works it cites.
Hand pose estimation through weakly-supervised learning of a rich intermediate representation
N. Neverova, C. Wolf, F. Nebout, and G. Taylor · 2017
Later among the works it cites.
Benchmarking and error diagnosis in multi-instance pose estimation
M. R. Ronchi and P. Perona · 2017
Later among the works it cites.
Benchmarking and error diagnosis in multi-instance pose estimation
M. R. Ronchi and P. Perona · 2017
Later among the works it cites.
Fully convolutional networks for semantic segmentation
E. Shelhamer, J. Long, and T. Darrell · 2017
Later among the works it cites.
Unsupervised object learning from dense equivariant image labelling
J. Thewlis, H. Bilen, and A. Vedaldi · 2017
Later among the works it cites.
Learning from synthetic humans
G. Varol, J. Romero, X. Martin, N. Mahmood, M. J. Black, I. Laptev, and C. Schmid · 2017
Later among the works it cites.
Total capture: A 3d deformation model for tracking faces, hands, and bodies
Y. S. Hanbyul Joo, Tomas Simon · 2018
Closest in time.