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Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods.
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2014
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2014
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2015
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2015
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2015
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2015
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2015
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2016
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2016
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2016
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2016
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2017
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2017
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B. X. Nie, P. Wei, and S.-C. Zhu, “Monocular 3d human pose estimation by predicting depth on joints,” in Proceedings of the IEEE International Conference on Computer Vision . IEEE, 2017, pp. 3467–3475
2017
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F. Moreno-Noguer, “3d human pose estimation from a single image via distance matrix regression,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2823–2832
2017
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B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose estimation and tracking,” in Proceedings of the European Conference on Computer Vision , 2018, pp. 466–481
2018
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H. Zhang, J. Cao, G. Lu, W. Ouyang, and Z. Sun, “Danet: Decompose-and-aggregate network for 3d human shape and pose estimation,” in Proceedings of the 27th ACM International Conference on Multimedia . ACM, 2019, pp. 935–944
2019
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R. A. Guler and I. Kokkinos, “Holopose: Holistic 3d human reconstruction in-the-wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 884–10 894
2019
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2019
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B. Tekin, P. Márquez-Neila, M. Salzmann, and P. Fua, “Learning to fuse 2d and 3d image cues for monocular body pose estimation,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3941–3950
2017
Cited alongside, same era.
G. Pavlakos, X. Zhou, K. G. Derpanis, and K. Daniilidis, “Coarse-to-fine volumetric prediction for single-image 3d human pose,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7025–7034
2017
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V. Tan, I. Budvytis, and R. Cipolla, “Indirect deep structured learning for 3d human body shape and pose prediction,” in British Machine Vision Conference , 2017, pp. 1–11
2017
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T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2017, pp. 1–14
2017
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X. Sun, J. Shang, S. Liang, and Y. Wei, “Compositional human pose regression,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2602–2611
2017
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X. Zhou, Q. Huang, X. Sun, X. Xue, and Y. Wei, “Towards 3d human pose estimation in the wild: a weakly-supervised approach,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 398–407
2017
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2017
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R. Alp Guler, G. Trigeorgis, E. Antonakos, P. Snape, S. Zafeiriou, and I. Kokkinos, “Densereg: Fully convolutional dense shape regression in-the-wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6799–6808
2017
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A. Kanazawa, J. Y. Zhang, P. Felsen, and J. Malik, “Learning 3d human dynamics from video,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5614–5623
2019
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2019
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2019
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2019
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