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We propose a novel algorithm for the fitting of 3D human shape to images.
Hadamard, J.: Sur les problèmes aux dérivées partielles et leur signification physique. Princeton university bulletin pp. 49–52 (1902)
1902
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Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: Scape: shape completion and animation of people. In: ACM transactions on graphics (TOG). vol. 24, pp. 408–416. ACM (2005)
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Sigal, L., Balan, A., Black, M.J.: Combined discriminative and generative articulated pose and non-rigid shape estimation. In: Advances in neural information processing systems. pp. 1337–1344 (2008)
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Guan, P., Weiss, A., Balan, A.O., Black, M.J.: Estimating human shape and pose from a single image. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 1381–1388. IEEE (2009)
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Hasler, N., Ackermann, H., Rosenhahn, B., Thormählen, T., Seidel, H.P.: Multilinear pose and body shape estimation of dressed subjects from image sets. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1823–1830. IEEE (2010)
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Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36
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Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2d human pose estimation: New benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. pp. 3686–3693 (2014)
2014
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Loper, M., Mahmood, N., Black, M.J.: Mosh: Motion and shape capture from sparse markers. ACM Transactions on Graphics (TOG) 33
2014
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Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: Smpl: A skinned multi-person linear model. ACM transactions on graphics (TOG) 34
2015
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Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M.W., Pfau, D., Schaul, T., Shillingford, B., De Freitas, N.: Learning to learn by gradient descent by gradient descent. In: Advances in neural information processing systems. pp. 3981–3989 (2016)
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Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it smpl: Automatic estimation of 3d human pose and shape from a single image. In: European Conference on Computer Vision. pp. 561–578. Springer (2016)
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Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European conference on computer vision. pp. 483–499. Springer (2016)
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Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P.V., Schiele, B.: Deepcut: Joint subset partition and labeling for multi person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4929–4937 (2016)
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2016
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Adler, J., Öktem, O.: Solving ill-posed inverse problems using iterative deep neural networks. Inverse Problems 33
2017
Cited alongside, same era.
Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M.J., Gehler, P.V.: Unite the people: Closing the loop between 3d and 2d human representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6050–6059 (2017)
2017
Cited alongside, same era.
Martinez, J., Hossain, R., Romero, J., Little, J.J.: A simple yet effective baseline for 3d human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2640–2649 (2017)
2017
Cited alongside, same era.
Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.P., Xu, W., Casas, D., Theobalt, C.: Vnect: Real-time 3d human pose estimation with a single rgb camera. ACM Transactions on Graphics (TOG) 36
2017
Cited alongside, same era.
Tan, V., Budvytis, I., Cipolla, R.: Indirect deep structured learning for 3d human body shape and pose prediction (2018)
2018
Later among the works it cites.
Varol, G., Ceylan, D., Russell, B., Yang, J., Yumer, E., Laptev, I., Schmid, C.: Bodynet: Volumetric inference of 3d human body shapes. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 20–36 (2018)
2018
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Arnab, A., Doersch, C., Zisserman, A.: Exploiting temporal context for 3d human pose estimation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3395–3404 (2019)
2019
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2019
Later among the works it cites.
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Tung, H.Y., Tung, H.W., Yumer, E., Fragkiadaki, K.: Self-supervised learning of motion capture. In: Advances in Neural Information Processing Systems. pp. 5236–5246 (2017)
2017
Cited alongside, same era.
Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M.J., Laptev, I., Schmid, C.: Learning from synthetic humans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 109–117 (2017)
2017
Cited alongside, same era.
Adler, J., Öktem, O.: Learned primal-dual reconstruction. IEEE transactions on medical imaging 37
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Joo, H., Simon, T., Sheikh, Y.: Total capture: A 3d deformation model for tracking faces, hands, and bodies. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8320–8329 (2018)
2018
Cited alongside, same era.
Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7122–7131 (2018)
2018
Cited alongside, same era.
von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3d human pose in the wild using imus and a moving camera. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 601–617 (2018)
2018
Cited alongside, same era.
Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., Schiele, B.: Neural body fitting: Unifying deep learning and model based human pose and shape estimation. In: 2018 International Conference on 3D Vision (3DV). pp. 484–494. IEEE (2018)
2018
Cited alongside, same era.
Guler, R.A., Kokkinos, I.: Holopose: Holistic 3d human reconstruction in-the-wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 10884–10894 (2019)
2019
Later among the works it cites.
Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3d human dynamics from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5614–5623 (2019)
2019
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Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3d human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2252–2261 (2019)
2019
Later among the works it cites.
Kolotouros, N., Pavlakos, G., Daniilidis, K.: Convolutional mesh regression for single-image human shape reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4501–4510 (2019)
2019
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Mahmood, N., Ghorbani, N., F. Troje, N., Pons-Moll, G., Black, M.J.: Amass: Archive of motion capture as surface shapes. In: The IEEE International Conference on Computer Vision (ICCV) (Oct 2019), https://amass.is.tue.mpg.de
2019
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Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A.A., Tzionas, D., Black, M.J.: Expressive body capture: 3d hands, face, and body from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 10975–10985 (2019)
2019
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Xiang, D., Joo, H., Sheikh, Y.: Monocular total capture: Posing face, body, and hands in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 10965–10974 (2019)
2019
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Xu, Y., Zhu, S.C., Tung, T.: Denserac: Joint 3d pose and shape estimation by dense render-and-compare. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 7760–7770 (2019)
2019
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Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: Deephuman: 3d human reconstruction from a single image. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 7739–7749 (2019)
2019
Later among the works it cites.