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The task of three-dimensional (3D) human pose estimation from a single image can be divided into two parts: (1) Two-dimensional (2D) human joint detection from the image and (2) estimating a 3D pose from the 2D joints.
Recovering non-rigid 3d shape from image streams
Bregler, C., Hertzmann, A., Biermann, H.: · 2000
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Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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Trajectory space: A dual representation for nonrigid structure from motion
Akhter, I., Sheikh, Y., Khan, S., Kanade, T.: · 2011
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Computing smooth time trajectories for camera and deformable shape in structure from motion with occlusion
Gotardo, P.F., Martinez, A.M.: · 2011
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Modec: Multimodal decomposable models for human pose estimation
Sapp, B., Taskar, B.: · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A.L., Hannun, A.Y., Ng, A.Y.: · 2013
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Deeppose: Human pose estimation via deep neural networks
Toshev, A., Szegedy, C.: · 2014
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Joint training of a convolutional network and a graphical model for human pose estimation
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: · 2014
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Articulated pose estimation by a graphical model with image dependent pairwise relations
Chen, X., Yuille, A.L.: · 2014
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Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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3d human pose estimation from monocular images with deep convolutional neural network
Li, S., Chan, A.B.: · 2014
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2d human pose estimation: New benchmark and state of the art analysis
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: · 2014
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Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 2014
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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. TPAMI (2014)
2014
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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: CVPR. (2014)
2014
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Efficient object localization using convolutional networks
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: · 2015
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Combining local appearance and holistic view: Dual-source deep neural networks for human pose estimation
Fan, X., Zheng, K., Lin, Y., Wang, S.: · 2015
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Procrustean normal distribution for non-rigid structure from motion
Lee, M., Cho, J., Oh, S.: · 2016
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Deep kinematic pose regression
Zhou, X., Sun, X., Zhang, W., Liang, S., Wei, Y.: · 2016
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Structured prediction of 3d human pose with deep neural networks
Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., Fua, P.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: ECCV. (2016)
2016
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Realtime multi-person 2d pose estimation using part affinity fields
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: · 2017
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Empirical evaluation of rectified activations in convolutional network
Xu, B., Wang, N., Chen, T., Li, M.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2015
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Chainer: a next-generation open source framework for deep learning
Tokui, S., Oono, K., Hido, S., Clayton, J.: · 2015
Cited alongside, same era.
Convolutional pose machines
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: · 2016
Cited alongside, same era.
Human pose estimation with iterative error feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: · 2016
Cited alongside, same era.
Human pose estimation via convolutional part heatmap regression
Bulat, A., Tzimiropoulos, G.: · 2016
Cited alongside, same era.
Monocular 3d human pose estimation in the wild using improved cnn supervision
Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., Theobalt, C.: · 2017
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A simple yet effective baseline for 3d human pose estimation
Martinez, J., Hossain, R., Romero, J., Little, J.J.: · 2017
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Lifting from the deep: Convolutional 3d pose estimation from a single image
Tome, D., Russell, C., Agapito, L.: · 2017
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Coarse-to-fine volumetric prediction for single-image 3d human pose
Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: · 2017
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Towards 3d human pose estimation in the wild: a weakly-supervised approach
Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y.: · 2017
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End-to-end recovery of human shape and pose
Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: · 2017
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Adversarial inverse graphics networks: Learning 2d-to-3d lifting and image-to-image translation from unpaired supervision
Tung, H.Y.F., Harley, A.W., Seto, W., Fragkiadaki, K.: · 2017
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