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Most of the previous image-based 3D human pose and mesh estimation methods estimate parameters of the human mesh model from an input image.
Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: BMVC (2010)
2010
Earlier work this paper cites.
Johnson, S., Everingham, M.: Learning effective human pose estimation from inaccurate annotation. In: CVPR (2011)
2011
Earlier work this paper cites.
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: New benchmark and state of the art analysis. In: CVPR (2014)
2014
Earlier work this paper cites.
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments. TPAMI (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ICLR (2014)
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
Earlier work this paper cites.
Loper, M., Mahmood, N., Black, M.J.: Mosh: Motion and shape capture from sparse markers. ACM TOG (2014)
2014
Earlier work this paper cites.
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: NeurIPS (2014)
2014
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating deep network training by reducing internal covariate shift. ICML (2015)
2015
Earlier work this paper cites.
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: A skinned multi-person linear model. ACM TOG (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. IJCV (2015)
2015
Earlier work this paper cites.
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: ECCV (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: ECCV (2016)
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV (2017)
2017
Earlier work this paper cites.
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: CVPR (2017)
2017
Earlier work this paper cites.
Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3D human pose estimation in the wild using improved cnn supervision. In: 3DV (2017)
2017
Earlier work this paper cites.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)
2017
Cited alongside, same era.
Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image 3D human pose. In: CVPR (2017)
2017
Cited alongside, same era.
Rogez, G., Weinzaepfel, P., Schmid, C.: LCR-Net: Localization-classification-regression for human pose. In: CVPR (2017)
2017
Cited alongside, same era.
Romero, J., Tzionas, D., Black, M.J.: Embodied hands: Modeling and capturing hands and bodies together. ACM TOG (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: CVPR (2017)
2017
Cited alongside, same era.
Arnab, A., Doersch, C., Zisserman, A.: Exploiting temporal context for 3D human pose estimation in the wild. In: CVPR (2019)
2019
Later among the works it cites.
Baek, S., In Kim, K., Kim, T.K.: Pushing the envelope for RGB-based dense 3D hand pose estimation via neural rendering. In: CVPR (2019)
2019
Later among the works it cites.
Boukhayma, A., de Bem, R., Torr, P.H.: 3D hand shape and pose from images in the wild. In: CVPR (2019)
2019
Later among the works it cites.
Christian Zimmermann, Duygu Ceylan, J.Y.B.R.M.A., Brox, T.: FreiHAND: A dataset for markerless capture of hand pose and shape from single RGB images. In: ICCV (2019)
2019
Later among the works it cites.
Ge, L., Ren, Z., Li, Y., Xue, Z., Wang, Y., Cai, J., Yuan, J.: 3D hand shape and pose estimation from a single RGB image. In: CVPR (2019)
2019
Later among the works it cites.
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Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: CVPR (2018)
2018
Cited alongside, same era.
Iqbal, U., Molchanov, P., Breuel Juergen Gall, T., Kautz, J.: Hand pose estimation via latent 2.5 d heatmap regression. In: ECCV (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: CVPR (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: ECCV (2018)
2018
Cited alongside, same era.
Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons-Moll, G., Theobalt, C.: Single-shot multi-person 3D pose estimation from monocular RGB. In: 3DV (2018)
2018
Cited alongside, same era.
Moon, G., Chang, J.Y., Lee, K.M.: V2V-PoseNet: Voxel-to-voxel prediction network for accurate 3D hand and human pose estimation from a single depth map. In: CVPR (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: 3DV. IEEE (2018)
2018
Cited alongside, same era.
Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M.J., Laptev, I., Schmid, C.: Learning joint reconstruction of hands and manipulated objects. In: CVPR (2019)
2019
Later among the works it cites.
Iskakov, K., Burkov, E., Lempitsky, V., Malkov, Y.: Learnable triangulation of human pose. In: ICCV (2019)
2019
Later among the works it cites.
Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3D human dynamics from video. In: CVPR (2019)
2019
Later among the works it cites.
Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: ICCV (2019)
2019
Later among the works it cites.
Kolotouros, N., Pavlakos, G., Daniilidis, K.: Convolutional mesh regression for single-image human shape reconstruction. In: CVPR (2019)
2019
Later among the works it cites.
Moon, G., Chang, J.Y., Lee, K.M.: Camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. In: ICCV (2019)
2019
Later among the works it cites.
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: CVPR (2019)
2019
Later among the works it cites.
Pavlakos, G., Kolotouros, N., Daniilidis, K.: TexturePose: Supervising human mesh estimation with texture consistency. In: ICCV (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Choi, H., Moon, G., Lee, K.M.: Pose2Mesh: Graph convolutional network for 3D human pose and mesh recovery from a 2D human pose. ECCV (2020)
2020
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