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Human pose and shape estimation (HPS) has attracted increasing attention in recent years.
J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, “Real-time human pose recognition in parts from single depth images,” in CVPR 2011 . Ieee, 2011, pp. 1297–1304
2011
Earlier work this paper cites.
F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, “Berkeley mhad: A comprehensive multimodal human action database,” in 2013 IEEE workshop on applications of computer vision (WACV) . IEEE, 2013, pp. 53–60
2013
Earlier work this paper cites.
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “SMPL: A skinned multi-person linear model,” ACM transactions on graphics (TOG) , vol. 34, no. 6, pp. 1–16, 2015
2015
Earlier work this paper cites.
A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European conference on computer vision . Springer, 2016, pp. 483–499
2016
Earlier work this paper cites.
F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero, and M. J. Black, “Keep it smpl: Automatic estimation of 3d human pose and shape from a single image,” in European conference on computer vision . Springer, 2016, pp. 561–578
2016
Earlier work this paper cites.
A. Haque, B. Peng, Z. Luo, A. Alahi, S. Yeung, and L. Fei-Fei, “Towards viewpoint invariant 3d human pose estimation,” in European conference on computer vision . Springer, 2016, pp. 160–177
2016
Earlier work this paper cites.
G. Varol, J. Romero, X. Martin, N. Mahmood, M. J. Black, I. Laptev, and C. Schmid, “Learning from synthetic humans,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 4627–4635, 2017
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Kanazawa, M. J. Black, D. W. Jacobs, and J. Malik, “End-to-end recovery of human shape and pose,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7122–7131
2018
Earlier work this paper cites.
Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. g. Yu, and J. Sun, “Cascaded pyramid network for multi-person pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7103–7112
2018
Earlier work this paper cites.
G. Pavlakos, L. Zhu, X. Zhou, and K. Daniilidis, “Learning to estimate 3d human pose and shape from a single color image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 459–468
2018
Earlier work this paper cites.
M. Omran, C. Lassner, G. Pons-Moll, P. Gehler, and B. Schiele, “Neural body fitting: Unifying deep learning and model based human pose and shape estimation,” in 2018 international conference on 3D vision (3DV) . IEEE, 2018, pp. 484–494
2018
Earlier work this paper cites.
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry, “3d-coded: 3d correspondences by deep deformation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 230–246
2018
Earlier work this paper cites.
M. Fabbri, F. Lanzi, S. Calderara, A. Palazzi, R. Vezzani, and R. Cucchiara, “Learning to detect and track visible and occluded body joints in a virtual world,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 430–446
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Kolotouros, G. Pavlakos, M. J. Black, and K. Daniilidis, “Learning to reconstruct 3d human pose and shape via model-fitting in the loop,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2252–2261
2019
Earlier work this paper cites.
H. Jiang, J. Cai, and J. Zheng, “Skeleton-aware 3d human shape reconstruction from point clouds,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5431–5441
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang et al. , “Hybrid task cascade for instance segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4974–4983
2019
Earlier work this paper cites.
K. Sun, B. Xiao, D. Liu, and J. Wang, “Deep high-resolution representation learning for human pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5693–5703
2019
Cited alongside, same era.
G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3d hands, face, and body from a single image,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 975–10 985
2019
Cited alongside, same era.
R. A. Guler and I. Kokkinos, “Holopose: Holistic 3d human reconstruction in-the-wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 884–10 894
2019
Cited alongside, same era.
N. Kolotouros, G. Pavlakos, and K. Daniilidis, “Convolutional mesh regression for single-image human shape reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4501–4510
G. Liu, Y. Rong, and L. Sheng, “Votehmr: Occlusion-aware voting network for robust 3d human mesh recovery from partial point clouds,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 955–964
2021
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2021
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S. Wang, A. Geiger, and S. Tang, “Locally aware piecewise transformation fields for 3d human mesh registration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7639–7648
2021
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2021
Later among the works it cites.
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2019
Cited alongside, same era.
A. Kanazawa, J. Y. Zhang, P. Felsen, and J. Malik, “Learning 3d human dynamics from video,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5614–5623
2019
Cited alongside, same era.
Y. Sun, Y. Ye, W. Liu, W. Gao, Y. Fu, and T. Mei, “Human mesh recovery from monocular images via a skeleton-disentangled representation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5349–5358
2019
Cited alongside, same era.
F. Xiong, B. Zhang, Y. Xiao, Z. Cao, T. Yu, J. T. Zhou, and J. Yuan, “A2j: Anchor-to-joint regression network for 3d articulated pose estimation from a single depth image,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 793–802
2019
Cited alongside, same era.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” Acm Transactions On Graphics (tog) , vol. 38, no. 5, pp. 1–12, 2019
2019
Cited alongside, same era.
X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 510–519
2019
Cited alongside, same era.
B. L. Bhatnagar, C. Sminchisescu, C. Theobalt, and G. Pons-Moll, “Combining implicit function learning and parametric models for 3d human reconstruction,” in European Conference on Computer Vision . Springer, 2020, pp. 311–329
2020
Cited alongside, same era.
A. A. A. Osman, T. Bolkart, and M. J. Black, “STAR: sparse trained articulated human body regressor,” in ECCV (6) , ser. Lecture Notes in Computer Science, vol. 12351. Springer, 2020, pp. 598–613
2020
Cited alongside, same era.
H. Xu, E. G. Bazavan, A. Zanfir, W. T. Freeman, R. Sukthankar, and C. Sminchisescu, “Ghum & ghuml: Generative 3d human shape and articulated pose models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6184–6193
2020
Cited alongside, same era.
J. Li, C. Xu, Z. Chen, S. Bian, L. Yang, and C. Lu, “Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation,” in CVPR . Computer Vision Foundation / IEEE, 2021, pp. 3383–3393
2021
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Y. Sun, Q. Bao, W. Liu, Y. Fu, B. Michael J., and T. Mei, “Monocular, one-stage, regression of multiple 3d people,” in ICCV , October 2021
2021
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S. K. Dwivedi, N. Athanasiou, M. Kocabas, and M. J. Black, “Learning to regress bodies from images using differentiable semantic rendering,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 250–11 259
2021
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N. Kolotouros, G. Pavlakos, D. Jayaraman, and K. Daniilidis, “Probabilistic modeling for human mesh recovery,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 605–11 614
2021
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H. Choi, G. Moon, and K. M. Lee, “Beyond static features for temporally consistent 3d human pose and shape from a video,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
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N. Garau, N. Bisagno, P. Bródka, and N. Conci, “Deca: Deep viewpoint-equivariant human pose estimation using capsule autoencoders,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 677–11 686
2021
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R. Bashirov, A. Ianina, K. Iskakov, Y. Kononenko, V. Strizhkova, V. Lempitsky, and A. Vakhitov, “Real-time rgbd-based extended body pose estimation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 2807–2816
2021
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Z. Shen, M. Zhang, H. Zhao, S. Yi, and H. Li, “Efficient attention: Attention with linear complexities,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2021, pp. 3531–3539
2021
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Z. Li, J. Liu, Z. Zhang, S. Xu, and Y. Yan, “Cliff: Carrying location information in full frames into human pose and shape estimation,” in European Conference on Computer Vision . Springer, 2022, pp. 590–606
2022
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Z. Cai, D. Ren, A. Zeng, Z. Lin, T. Yu, W. Wang, X. Fan, Y. Gao, Y. Yu, L. Pan, F. Hong, M. Zhang, C. C. Loy, L. Yang, and Z. Liu, “Humman: Multi-modal 4d human dataset for versatile sensing and modeling,” October 2022
2022
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J. Li, J. Zhang, Z. Wang, S. Shen, C. Wen, Y. Ma, L. Xu, J. Yu, and C. Wang, “Lidarcap: Long-range marker-less 3d human motion capture with lidar point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 20 502–20 512
2022
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Y. Dai, Y. Lin, C. Wen, S. Shen, L. Xu, J. Yu, Y. Ma, and C. Wang, “Hsc4d: Human-centered 4d scene capture in large-scale indoor-outdoor space using wearable imus and lidar,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6792–6802
2022
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2022
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J. Rajasegaran, G. Pavlakos, A. Kanazawa, and J. Malik, “Tracking people by predicting 3d appearance, location and pose,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2740–2749
2022
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Y. Sun, W. Liu, Q. Bao, Y. Fu, T. Mei, and M. J. Black, “Putting people in their place: Monocular regression of 3d people in depth,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 243–13 252
2022
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