Deep pose consensus networks
Tài liệu tham khảo
Andriluka, 2014, 2D human pose estimation: New benchmark and state of the art analysis
Bo, 2010, Twin gaussian processes for structured prediction, Int. J. Comput. Vis., 87, 28, 10.1007/s11263-008-0204-y
Bogo, 2016, Keep it smpl: Automatic estimation of 3D human pose and shape from a single image, 561
Bosch, 2007, Representing shape with a spatial pyramid kernel, 401
Bourdev, 2009, Poselets: Body part detectors trained using 3D human pose annotations, 1365
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Chang, J.Y., Lee, K.M., 2017. 2D-3D pose consistency-based conditional random fields for 3D human pose estimation, arXiv preprint arXiv:1704.03986.
Chen, 2017, 3D human pose estimation= 2D pose estimation+ matching, 7035
Chen, 2016, Synthesizing training images for boosting human 3D pose estimation, 479
Dalal, 2005, Histograms of oriented gradients for human detection, 886
Du, 2016, Marker-less 3D human motion capture with monocular image sequence and height-maps, 20
Fang, 2018, Learning pose grammar to encode human body configuration for 3D pose estimation
Ghezelghieh, 2016, Learning camera viewpoint using cnn to improve 3D body pose estimation, 685
Ionescu, 2014, Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments, IEEE Trans. Pattern Anal. Mach. Intell., 36, 1325, 10.1109/TPAMI.2013.248
Kostrikov, 2014, Depth sweep regression forests for estimating 3D human pose from images
Lee, 2016, Consensus of non-rigid reconstructions, 4670
Lin, Z., Chen, M., Ma, Y., 2010. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055.
Martinez, 2017, A simple yet effective baseline for 3d human pose estimation
Moreno-Noguer, 2017, 3D human pose estimation from a single image via distance matrix regression, Proc. of the IEEE Computer Vision and Pattern Recognition
Newell, 2016, Stacked hourglass networks for human pose estimation, 483
Park, 2016, 3D human pose estimation using convolutional neural networks with 2D pose information, 156
Pavlakos, 2017, Coarse-to-fine volumetric prediction for single-image 3D human pose, 1263
Rogez, 2016, Mocap-guided data augmentation for 3D pose estimation in the wild, 3108
Sanzari, 2016, Bayesian image based 3D pose estimation, 566
Sigal, 2010, Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion, Int. J. Comput. Vis., 87, 4, 10.1007/s11263-009-0273-6
Tekin, 2016, Structured prediction of 3D human pose with deep neural networks
Tieleman, 2012, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Netw. Mach. Learn
Tome, 2017, Lifting from the deep: Convolutional 3D pose estimation from a single image, 1263
Toshev, 2014, Deeppose: Human pose estimation via deep neural networks
Viola, 2004, Robust real-time face detection, Int. J. Comput. Vis., 57, 137, 10.1023/B:VISI.0000013087.49260.fb
Wang, 2013, An approach to pose-based action recognition, 915
Wei, 2016, Convolutional pose machines
Wu, 2016, Single image 3D interpreter network, 365
Yang, 2013, Articulated human detection with flexible mixtures of parts, IEEE Trans. Pattern Anal. Mach. Intell., 35, 2878, 10.1109/TPAMI.2012.261
Yasin, 2016, A dual-source approach for 3D pose estimation from a single image, 4948
Zhou, 2016, Deep kinematic pose regression, 186
Zhou, 2017, Sparse representation for 3D shape estimation: A convex relaxation approach, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1648, 10.1109/TPAMI.2016.2605097
Zhou, 2016, Sparseness meets deepness: 3D human pose estimation from monocular video, 4966