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Landmark/pose estimation in single monocular images have received much effort in computer vision due to its important applications.
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2012
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L. Pishchulin, M. Andriluka, P. V. Gehler, and B. Schiele, “Strong appearance and expressive spatial models for human pose estimation,” in Proc. IEEE Int. Conf. Comp. Vis. , 2013, pp. 3487–3494
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B. Sapp and B. Taskar, “MODEC: Multimodal decomposable models for human pose estimation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2013, pp. 3674–3681
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X. P. Burgos-Artizzu, P. Perona, and P. Dollár, “Robust face landmark estimation under occlusion,” in Proc. IEEE Int. Conf. Comp. Vis. , 2013, pp. 1513–1520
2013
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X. Xiong and F. De la Torre, “Supervised descent method and its applications to face alignment,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2013, pp. 532–539
2013
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Y. Sun, X. Wang, and X. Tang, “Deep convolutional network cascade for facial point detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2013, pp. 3476–3483
2013
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P. N. Belhumeur, D. W. Jacobs, D. J. Kriegman, and N. Kumar, “Localizing parts of faces using a consensus of exemplars,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 12, pp. 2930–2940, 2013
2013
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C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “300 faces in-the-wild challenge: The first facial landmark localization challenge,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2013, pp. 397–403
2013
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J. Yan, Z. Lei, D. Yi, and S. Li, “Learn to combine multiple hypotheses for accurate face alignment,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2013, pp. 392–396
2013
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E. Zhou, H. Fan, Z. Cao, Y. Jiang, and Q. Yin, “Extensive facial landmark localization with coarse-to-fine convolutional network cascade,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2013, pp. 386–391
2013
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Y. Yang and D. Ramanan, “Articulated human detection with flexible mixtures of parts,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 12, pp. 2878–2890, 2013
2013
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J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” in Proc. Advances in Neural Inf. Process. Syst. , 2014, pp. 1799–1807
2014
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A. Toshev and C. Szegedy, “DeepPose: human pose estimation via deep neural networks,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014, pp. 1653–1660
2014
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I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in Proc. Advances in Neural Inf. Process. Syst. , 2014, pp. 2672–2680
2014
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2014
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X. Cao, Y. Wei, F. Wen, and J. Sun, “Face alignment by explicit shape regression,” Int. J. Comput. Vision , vol. 107, no. 2, pp. 177–190, 2014
2014
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S. Ren, X. Cao, Y. Wei, and J. Sun, “Face alignment at 3000 fps via regressing local binary features,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014, pp. 1685–1692
2014
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J. Zhang, S. Shan, M. Kan, and X. Chen, “Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment,” in Proc. Eur. Conf. Comp. Vis. Springer, 2014, pp. 1–16
2014
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2014
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2014
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M. Andriluka, L. Pishchulin, P. V. Gehler, and B. Schiele, “2D human pose estimation: New benchmark and state of the art analysis,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014, pp. 3686–3693
2014
Cited alongside, same era.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proc. Eur. Conf. Comp. Vis. Springer, 2014, pp. 740–755
2014
Cited alongside, same era.
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 7, pp. 1325–1339, 2014
2014
Cited alongside, same era.
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, “Efficient object localization using convolutional networks,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 648–656
2015
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition.” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016, pp. 770–778
2016
Later among the works it cites.
S. Zhu, C. Li, C.-C. Loy, and X. Tang, “Unconstrained face alignment via cascaded compositional learning,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016, pp. 3409–3417
2016
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J. Deng, Q. Liu, J. Yang, and D. Tao, “M 3 csr: multi-view, multi-scale and multi-component cascade shape regression,” Image and Vision Computing , vol. 47, pp. 19–26, 2016
2016
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H. Fan and E. Zhou, “Approaching human level facial landmark localization by deep learning,” Image and Vision Computing , vol. 47, pp. 27–35, 2016
2016
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B. Martinez and M. F. Valstar, “L 2, 1-based regression and prediction accumulation across views for robust facial landmark detection,” Image and Vision Computing , vol. 47, pp. 36–44, 2016
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A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv , vol. 1511.06434, pp. 1–16, 2015
2015
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E. L. Denton, S. Chintala, A. Szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015, pp. 1486–1494
2015
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015, pp. 91–99
2015
Cited alongside, same era.
G. Tzimiropoulos, “Project-out cascaded regression with an application to face alignment,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 3659–3667
2015
Cited alongside, same era.
Y. Chen, W. Luo, and J. Yang, “Facial landmark detection via pose-induced auto-encoder networks,” in Image Processing (ICIP), 2015 IEEE International Conference on . IEEE, 2015, pp. 2115–2119
2015
Cited alongside, same era.
S. Zhu, C. Li, C. Change Loy, and X. Tang, “Face alignment by coarse-to-fine shape searching,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 4998–5006
2015
Cited alongside, same era.
G. G. Chrysos, E. Antonakos, S. Zafeiriou, and P. Snape, “Offline deformable face tracking in arbitrary videos,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2015, pp. 1–9
2015
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J. Shen, S. Zafeiriou, G. G. Chrysos, J. Kossaifi, G. Tzimiropoulos, and M. Pantic, “The first facial landmark tracking in-the-wild challenge: Benchmark and results,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2015, pp. 50–58
2015
Cited alongside, same era.
2016
Later among the works it cites.
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik, “Human pose estimation with iterative error feedback,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016, pp. 4733–4742
2016
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P. Hu and D. Ramanan, “Bottom-up and top-down reasoning with hierarchical rectified Gaussians,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016, pp. 5600–5609
2016
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G. Gkioxari, A. Toshev, and N. Jaitly, “Chained predictions using convolutional neural networks,” in Proc. Eur. Conf. Comp. Vis. , 2016, pp. 728–743
2016
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U. Rafi, I. Kostrikov, J. Gall, and B. Leibe, “An efficient convolutional network for human pose estimation,” in Proc. British Machine Vis. Conf. , 2016, pp. 1–11
2016
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B. Tekin, A. Rozantsev, V. Lepetit, and P. Fua, “Direct prediction of 3d body poses from motion compensated sequences,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016, pp. 991–1000
2016
Later among the works it cites.
M. F. Ghezelghieh, R. Kasturi, and S. Sarkar, “Learning camera viewpoint using cnn to improve 3d body pose estimation,” in 3D Vision (3DV), 2016 Fourth International Conference on . IEEE, 2016, pp. 685–693
2016
Later among the works it cites.
Y. Du, Y. Wong, Y. Liu, F. Han, Y. Gui, Z. Wang, M. Kankanhalli, and W. Geng, “Marker-less 3d human motion capture with monocular image sequence and height-maps,” in Proc. Eur. Conf. Comp. Vis. Springer, 2016, pp. 20–36
2016
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S. Park, J. Hwang, and N. Kwak, “3d human pose estimation using convolutional neural networks with 2d pose information,” in Computer Vision–ECCV 2016 Workshops . Springer, 2016, pp. 156–169
2016
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A. Jourabloo, M. Ye, X. Liu, and L. Ren, “Pose-invariant face alignment with a single cnn,” in Proc. IEEE Int. Conf. Comp. Vis. IEEE, 2017, pp. 3219–3228
2017
Closest in time.
M. Kowalski, J. Naruniec, and T. Trzcinski, “Deep alignment network: A convolutional neural network for robust face alignment,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn. Workshop , 2017
2017
Closest in time.
J. Zhao, M. Mathieu, and Y. LeCun, “Energy-based generative adversarial network,” in Proc. Int. Conf. Learn. Representations , 2017
2017
Closest in time.
A. Bulat and G. Tzimiropoulos, “How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3d facial landmarks),” in Proc. IEEE Int. Conf. Comp. Vis. , vol. 1, no. 6, 2017, p. 8
2017
Closest in time.
X. Chu, W. Ouyang, H. Li, and X. Wang, “Multi-context attention for human pose estimation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2017
2017
Closest in time.
S. Huang, M. Gong, and D. Tao, “A coarse-fine network for keypoint localization,” in Proc. IEEE Int. Conf. Comp. Vis. , vol. 2, 2017
2017
Closest in time.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proc. IEEE Int. Conf. Comp. Vis. IEEE, 2017, pp. 2980–2988
2017
Closest in time.
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , vol. 1, no. 2, 2017, p. 7
2017
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H.-S. Fang, S. Xie, Y.-W. Tai, and C. Lu, “Rmpe: Regional multi-person pose estimation,” in Proc. IEEE Int. Conf. Comp. Vis. , 2017
2017
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F. Moreno-Noguer, “3d human pose estimation from a single image via distance matrix regression,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2017
2017
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J. Martinez, R. Hossain, J. Romero, and J. J. Little, “A simple yet effective baseline for 3d human pose estimation,” Proc. IEEE Int. Conf. Comp. Vis. , 2017
2017
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J. Lv, X. Shao, J. Xing, C. Cheng, and X. Zhou, “A deep regression architecture with two-stage re-initialization for high performance facial landmark detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2017
2017
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V. Belagiannis and A. Zisserman, “Recurrent human pose estimation,” in Proc. IEEE Int. Automatic Face & Gesture Recognition , 2017
2017
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C.-J. Chou, J.-T. Chien, and H.-T. Chen, “Self adversarial training for human pose estimation,” arXiv: Comp. Res. Repository , vol. 1707.02439, 2017
2017
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , vol. 1, no. 2, 2017, p. 4
2017
Closest in time.
W. Yang, S. Li, W. Ouyang, H. Li, and X. Wang, “Learning feature pyramids for human pose estimation,” in Proc. IEEE Int. Conf. Comp. Vis. , vol. 2, no. 7, 2017
2017
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G. Papandreou, T. Zhu, N. Kanazawa, A. Toshev, J. Tompson, C. Bregler, and K. Murphy, “Towards accurate multiperson pose estimation in the wild,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , vol. 8, 2017
2017
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X. Zhou, M. Zhu, S. Leonardos, and K. Daniilidis, “Sparse representation for 3d shape estimation: A convex relaxation approach,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 8, pp. 1648–1661, 2017
2017
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Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. Yu, and J. Sun, “Cascaded pyramid network for multi-person pose estimation,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2018
2018
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W. Yang, W. Ouyang, X. Wang, J. Ren, H. Li, and X. Wang, “3d human pose estimation in the wild by adversarial learning,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2018
2018
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X. Dong, Y. Yan, W. Ouyang, and Y. Yang, “Style aggregated network for facial landmark detection,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2018
2018
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L. Ke, M.-C. Chang, H. Qi, and S. Lyu, “Multi-scale structure-aware network for human pose estimation,” in Proc. Eur. Conf. Comp. Vis. , 2018
2018
Closest in time.