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Image- and video-based 3D human recovery (i.e., pose and shape estimation) have achieved substantial progress.
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2014
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M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, “2d human pose estimation: New benchmark and state of the art analysis,” in CVPR , 2014, pp. 3686–3693
2014
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2014
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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
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2015
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2016
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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 ECCV . Springer, 2016, pp. 561–578
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C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,” in ICCV , 2017, pp. 843–852
2017
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S. R. Richter, Z. Hayder, and V. Koltun, “Playing for benchmarks,” in ICCV , 2017, pp. 2213–2222
2017
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D. Mehta, H. Rhodin, D. Casas, P. Fua, O. Sotnychenko, W. Xu, and C. Theobalt, “Monocular 3d human pose estimation in the wild using improved cnn supervision,” in 3DV . IEEE, 2017, pp. 506–516
2017
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S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in CVPR , 2017, pp. 1492–1500
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,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2640–2649
2017
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Y. Huang, F. Bogo, C. Lassner, A. Kanazawa, P. V. Gehler, J. Romero, I. Akhter, and M. J. Black, “Towards accurate marker-less human shape and pose estimation over time,” in 3DV . IEEE, 2017, pp. 421–430
2017
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C. Lassner, J. Romero, M. Kiefel, F. Bogo, M. J. Black, and P. V. Gehler, “Unite the people: Closing the loop between 3d and 2d human representations,” in CVPR , 2017, pp. 6050–6059
2017
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M. Trumble, A. Gilbert, C. Malleson, A. Hilton, and J. Collomosse, “Total capture: 3d human pose estimation fusing video and inertial sensors,” in BMVC , 2017
2017
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G. Varol, J. Romero, X. Martin, N. Mahmood, M. J. Black, I. Laptev, and C. Schmid, “Learning from synthetic humans,” CVPR , pp. 4627–4635, 2017
2017
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J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in ICCV , 2017, pp. 2223–2232
2017
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M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in ICML . PMLR, 2017, pp. 2208–2217
2017
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T. von Marcard, R. Henschel, M. J. Black, B. Rosenhahn, and G. Pons-Moll, “Recovering accurate 3d human pose in the wild using imus and a moving camera,” in ECCV , 2018, pp. 601–617
2018
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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 ECCV , 2018, pp. 430–446
2018
Cited alongside, same era.
A. Kanazawa, M. J. Black, D. W. Jacobs, and J. Malik, “End-to-end recovery of human shape and pose,” in CVPR , 2018, pp. 7122–7131
2018
Cited alongside, same era.
G. Pavlakos, L. Zhu, X. Zhou, and K. Daniilidis, “Learning to estimate 3d human pose and shape from a single color image,” in CVPR , 2018, pp. 459–468
2018
Cited alongside, same era.
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 3DV . IEEE, 2018, pp. 484–494
D. Mehta, O. Sotnychenko, F. Mueller, W. Xu, M. Elgharib, P. Fua, H.-P. Seidel, H. Rhodin, G. Pons-Moll, and C. Theobalt, “Xnect: Real-time multi-person 3d motion capture with a single rgb camera,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 82–1, 2020
2020
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G. Moon and K. M. Lee, “I2l-meshnet: Image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single RGB image,” in ECCV (7) , ser. Lecture Notes in Computer Science, vol. 12352. Springer, 2020, pp. 752–768
2020
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Z. Luo, S. A. Golestaneh, and K. M. Kitani, “3d human motion estimation via motion compression and refinement,” in Proceedings of the Asian Conference on Computer Vision , 2020
2020
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J. Song, X. Chen, and O. Hilliges, “Human body model fitting by learned gradient descent,” in ECCV (20) , ser. Lecture Notes in Computer Science, vol. 12365. Springer, 2020, pp. 744–760
2020
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2018
Cited alongside, same era.
D. Mehta, O. Sotnychenko, F. Mueller, W. Xu, S. Sridhar, G. Pons-Moll, and C. Theobalt, “Single-shot multi-person 3d pose estimation from monocular rgb,” 3DV , pp. 120–130, 2018
2018
Cited alongside, same era.
P. Krähenbühl, “Free supervision from video games,” in CVPR , 2018, pp. 2955–2964
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Rong, Z. Liu, C. Li, K. Cao, and C. C. Loy, “Delving deep into hybrid annotations for 3d human recovery in the wild,” in ICCV , 2019, pp. 5340–5348
2019
Cited alongside, same era.
H. Joo, T. Simon, X. Li, H. Liu, L. Tan, L. Gui, S. Banerjee, T. Godisart, B. C. Nabbe, I. Matthews, T. Kanade, S. Nobuhara, and Y. Sheikh, “Panoptic studio: A massively multiview system for social interaction capture,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, pp. 190–204, 2019
2019
Cited alongside, same era.
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 ICCV , 2019, pp. 2252–2261
2019
Cited alongside, same era.
D. Pavllo, C. Feichtenhofer, D. Grangier, and M. Auli, “3d human pose estimation in video with temporal convolutions and semi-supervised training,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 7753–7762
2019
Cited alongside, same era.
Later among the works it cites.
A. Sengupta, R. Cipolla, and I. Budvytis, “Synthetic training for accurate 3d human pose and shape estimation in the wild,” in BMVC . BMVA Press, 2020
2020
Later among the works it cites.
T. Zhang, B. Huang, and Y. Wang, “Object-occluded human shape and pose estimation from a single color image,” CVPR , pp. 7374–7383, 2020
2020
Later among the works it cites.
I. Kissos, L. Fritz, M. Goldman, O. Meir, E. Oks, and M. Kliger, “Beyond weak perspective for monocular 3d human pose estimation,” in ECCV . Springer, 2020, pp. 541–554
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Cai, J. Zhang, D. Ren, C. Yu, H. Zhao, S. Yi, C. K. Yeo, and C. C. Loy, “Messytable: Instance association in multiple camera views,” in ECCV . Springer, 2020, pp. 1–16
2020
Later among the works it cites.
2020
Later among the works it cites.
Y.-T. Hu, J. Wang, R. A. Yeh, and A. G. Schwing, “Sail-vos 3d: A synthetic dataset and baselines for object detection and 3d mesh reconstruction from video data,” in CVPR , 2021, pp. 1418–1428
2021
Closest in time.
M. Fabbri, G. Brasó, G. Maugeri, O. Cetintas, R. Gasparini, A. Ošep, S. Calderara, L. Leal-Taixé, and R. Cucchiara, “Motsynth: How can synthetic data help pedestrian detection and tracking?” in ICCV , 2021, pp. 10 849–10 859
2021
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P. Patel, C.-H. P. Huang, J. Tesch, D. T. Hoffmann, S. Tripathi, and M. J. Black, “AGORA: Avatars in geography optimized for regression analysis,” in CVPR , Jun. 2021
2021
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2021
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR . OpenReview.net, 2021
2021
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H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in ICML , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 2021, pp. 10 347–10 357
2021
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T. Fan, K. V. Alwala, D. Xiang, W. Xu, T. Murphey, and M. Mukadam, “Revitalizing optimization for 3d human pose and shape estimation: A sparse constrained formulation,” ICCV , 2021
2021
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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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H. Choi, G. Moon, and K. M. Lee, “Beyond static features for temporally consistent 3d human pose and shape from a video,” in CVPR , 2021
2021
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M. Contributors, “Openmmlab 3d human parametric model toolbox and benchmark,” https://github.com/open-mmlab/mmhuman3d , 2021
2021
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X. Chen, S. Wang, J. Wang, and M. Long, “Representation subspace distance for domain adaptation regression,” in ICML . PMLR, 2021, pp. 1749–1759
2021
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2021
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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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S. Zhang, Q. Ma, Y. Zhang, Z. Qian, T. Kwon, M. Pollefeys, F. Bogo, and S. Tang, “Egobody: Human body shape and motion of interacting people from head-mounted devices,” in European Conference on Computer Vision . Springer, 2022, pp. 180–200
2022
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