{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:28Z","timestamp":1740108028958,"version":"3.37.3"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671397"],"award-info":[{"award-number":["61671397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05086-0","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T11:02:48Z","timestamp":1592996568000},"page":"2165-2181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SDM3d: shape decomposition of multiple geometric priors for 3D pose estimation"],"prefix":"10.1007","volume":"33","author":[{"given":"Mengxi","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuliang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunqi","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"issue":"9","key":"5086_CR1","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1109\/TPAMI.2017.2752710","volume":"40","author":"A Agudo","year":"2017","unstructured":"Agudo A, Moreno-Noguer F (2017) Force-based representation for non-rigid shape and elastic model estimation. IEEE Trans Pattern Anal Mach Intell 40(9):2137\u20132150","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5086_CR2","doi-asserted-by":"crossref","unstructured":"Akhter I, Black MJ (2015) Pose-conditioned joint angle limits for 3D human pose reconstruction. In: Computer vision and pattern recognition, pp 1446\u20131455","DOI":"10.1109\/CVPR.2015.7298751"},{"key":"5086_CR3","doi-asserted-by":"crossref","unstructured":"Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2D human pose estimation: new benchmark and state of the art analysis. In: Computer vision and pattern recognition, pp 3686\u20133693","DOI":"10.1109\/CVPR.2014.471"},{"issue":"1\u20132","key":"5086_CR4","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s11263-008-0204-y","volume":"87","author":"L Bo","year":"2010","unstructured":"Bo L, Sminchisescu C (2010) Twin gaussian processes for structured prediction. Int J Comput Vis 87(1\u20132):28\u201352","journal-title":"Int J Comput Vis"},{"key":"5086_CR5","doi-asserted-by":"crossref","unstructured":"Bogo F, Kanazawa A, Lassner C, Gehler P, Romero J, Black MJ (2016) Keep it smpl: automatic estimation of 3D human pose and shape from a single image. In: European conference on computer vision, pp 561\u2013578","DOI":"10.1007\/978-3-319-46454-1_34"},{"issue":"1","key":"5086_CR6","first-page":"1455","volume":"15","author":"N Boumal","year":"2013","unstructured":"Boumal N, Mishra B, Absil PA, Sepulchre R (2013) Manopt, a matlab toolbox for optimization on manifolds. J Mach Learn Res 15(1):1455\u20131459","journal-title":"J Mach Learn Res"},{"issue":"1","key":"5086_CR7","first-page":"1","volume":"3","author":"SP Boyd","year":"2011","unstructured":"Boyd SP, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn Arch 3(1):1\u2013122","journal-title":"Found Trends Mach Learn Arch"},{"issue":"12","key":"5086_CR8","doi-asserted-by":"publisher","first-page":"5406","DOI":"10.1109\/TIT.2006.885507","volume":"52","author":"EJ Candes","year":"2006","unstructured":"Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inform Theory 52(12):5406\u20135425","journal-title":"IEEE Trans Inform Theory"},{"key":"5086_CR9","doi-asserted-by":"crossref","first-page":"e1915","DOI":"10.1002\/cav.1915","volume":"31","author":"W Cao","year":"2019","unstructured":"Cao W, Yang Z, Ren X, Lyu L, Zhang B, Zhang Y, Wu E (2019) An improved solution for deformation simulation of nonorthotropic geometric models. Comput Anim Virtual Worlds 31:e1915","journal-title":"Comput Anim Virtual Worlds"},{"key":"5086_CR10","doi-asserted-by":"crossref","unstructured":"Chen CH, Ramanan D (2017) 3D human pose estimation = 2D pose estimation+ matching. In: Computer vision and pattern recognition, pp 5759\u20135767","DOI":"10.1109\/CVPR.2017.610"},{"key":"5086_CR11","doi-asserted-by":"crossref","unstructured":"Chen W, Wang H, Li Y, Su H, Wang Z, Tu C, Lischinski D, Cohen-Or D, Chen B (2016) Synthesizing training images for boosting human 3D pose estimation. In: International conference on 3d vision, pp 479\u2013488","DOI":"10.1109\/3DV.2016.58"},{"key":"5086_CR12","unstructured":"CMU (2014) Mocap: Carnegie mellon university motion capture database. http:\/\/mocap.cs.cmu.edu\/"},{"issue":"1","key":"5086_CR13","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1006\/cviu.1995.1004","volume":"61","author":"TF Cootes","year":"1995","unstructured":"Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38\u201359","journal-title":"Comput Vis Image Underst"},{"key":"5086_CR14","unstructured":"Dai Y, Li H, He M (2012) A simple prior-free method for non-rigid structure-from-motion factorization. In: CVPR, pp 2018\u20132025"},{"key":"5086_CR15","doi-asserted-by":"crossref","unstructured":"Du Y, Wong Y, Liu Y, Han F, Gui Y, Wang Z, Kankanhalli M, Geng W (2016) Marker-less 3D human motion capture with monocular image sequence and height-maps. In: European conference on computer vision, pp 20\u201336","DOI":"10.1007\/978-3-319-46493-0_2"},{"key":"5086_CR16","doi-asserted-by":"crossref","unstructured":"Ehlers K, Brama K (2016) A human-robot interaction interface for mobile and stationary robots based on real-time 3d human body and hand-finger pose estimation. In: IEEE international conference on emerging technologies and factory automation, pp 1\u20136","DOI":"10.1109\/ETFA.2016.7733719"},{"key":"5086_CR17","doi-asserted-by":"crossref","unstructured":"Fan X, Zheng K, Zhou Y, Wang S (2014) Pose locality constrained representation for 3D human pose reconstruction. In: European conference on computer vision, pp 174\u2013188","DOI":"10.1007\/978-3-319-10590-1_12"},{"key":"5086_CR18","doi-asserted-by":"crossref","unstructured":"Hachiuma R, Saito H (2016) Recognition and pose estimation of primitive shapes from depth images for spatial augmented reality. In: 2016 IEEE 2nd workshop on everyday virtual reality, pp 32\u201335","DOI":"10.1109\/WEVR.2016.7859541"},{"issue":"7","key":"5086_CR19","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2014","unstructured":"Ionescu C, Papava D, Olaru V, Sminchisescu C (2014) Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans Pattern Anal Mach Intell 36(7):1325\u20131339","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5086_CR20","doi-asserted-by":"crossref","unstructured":"Jiang H (2010) 3D human pose reconstruction using millions of exemplars. In: International conference on pattern recognition, pp 1674\u20131677","DOI":"10.1109\/ICPR.2010.414"},{"issue":"C","key":"5086_CR21","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2019.05.034","volume":"358","author":"M Jiang","year":"2019","unstructured":"Jiang M, Yu Z, Zhang Y, Wang Q, Li C, Lei Y (2019) Reweighted sparse representation with residual compensation for 3d human pose estimation from a single rgb image. Neurocomputing 358(C):332\u2013343","journal-title":"Neurocomputing"},{"issue":"12","key":"5086_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-018-1066-6","volume":"126","author":"I Katircioglu","year":"2018","unstructured":"Katircioglu I, Tekin B, Salzmann M, Lepetit V, Fua P (2018) Learning latent representations of 3D human pose with deep neural networks. Int J Comput Vis 126(12):1\u201316","journal-title":"Int J Comput Vis"},{"key":"5086_CR23","doi-asserted-by":"crossref","unstructured":"Kostrikov I, Gall J (2014) Depth sweep regression forests for estimating 3D human pose from images. In: British machine vision conference, pp 1\u201313","DOI":"10.5244\/C.28.80"},{"key":"5086_CR24","doi-asserted-by":"crossref","unstructured":"Lawrence ND, Moore AJ (2007) Hierarchical gaussian process latent variable models. In: International conference on machine learning, pp 481\u2013488","DOI":"10.1145\/1273496.1273557"},{"issue":"1","key":"5086_CR25","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s11263-016-0962-x","volume":"122","author":"S Li","year":"2017","unstructured":"Li S, Zhang W, Chan AB (2017) Maximum-margin structured learning with deep networks for 3D human pose estimation. Int J Comput Vis 122(1):149\u2013168","journal-title":"Int J Comput Vis"},{"key":"5086_CR26","doi-asserted-by":"crossref","unstructured":"Lin M, Liang L, Liang X, Wang K, Hui C, Lin M, Liang L, Liang X, Wang K, Hui C (2017) Recurrent 3D pose sequence machines. In: Computer vision and pattern recognition, pp 5543\u20135552","DOI":"10.1109\/CVPR.2017.588"},{"issue":"8","key":"5086_CR27","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1007\/s00521-015-1863-6","volume":"26","author":"Z Liu","year":"2015","unstructured":"Liu Z, Song X, Tang Z (2015) Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition. Neural Comput Appl 26(8):2013\u20132026","journal-title":"Neural Comput Appl"},{"key":"5086_CR28","doi-asserted-by":"crossref","unstructured":"Lv Z (2019) Robust3d: a robust 3d face reconstruction application. In: Neural computing and applications, pp 1\u20138","DOI":"10.1007\/s00521-019-04380-w"},{"key":"5086_CR29","doi-asserted-by":"crossref","unstructured":"Martinez J, Hossain R, Romero J, Little JJ (2017) A simple yet effective baseline for 3D human pose estimation. In: International conference on computer vision, pp 2659\u20132668","DOI":"10.1109\/ICCV.2017.288"},{"key":"5086_CR30","doi-asserted-by":"crossref","unstructured":"Mehta D, Rhodin H, Casas D, Fua P, Sotnychenko O, Xu W, Theobalt C (2017) Monocular 3d human pose estimation in the wild using improved cnn supervision, pp 506\u2013516","DOI":"10.1109\/3DV.2017.00064"},{"key":"5086_CR31","doi-asserted-by":"crossref","unstructured":"Morenonoguer F (2017) 3D human pose estimation from a single image via distance matrix regression. In: Computer vision and pattern recognition, pp 1561\u20131570","DOI":"10.1109\/CVPR.2017.170"},{"key":"5086_CR32","doi-asserted-by":"crossref","unstructured":"Morozov AA, Sushkova OS, Polupanov AF (2017) Object-oriented logic programming of 3d intelligent video surveillance: the problem statement. In: IEEE 26th international symposium on industrial electronics, pp 1631\u20131636","DOI":"10.1109\/ISIE.2017.8001491"},{"issue":"1","key":"5086_CR33","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s10107-012-0629-5","volume":"140","author":"Yu Nesterov","year":"2013","unstructured":"Nesterov Yu (2013) Gradient methods for minimizing composite functions. Math Program 140(1):125\u2013161","journal-title":"Math Program"},{"key":"5086_CR34","doi-asserted-by":"crossref","unstructured":"Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European conference on computer vision, pp 483\u2013499","DOI":"10.1007\/978-3-319-46484-8_29"},{"issue":"23","key":"5086_CR35","doi-asserted-by":"publisher","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","volume":"37","author":"BA Olshausen","year":"1997","unstructured":"Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by v1? Vis Res 37(23):3311\u20133325","journal-title":"Vis Res"},{"key":"5086_CR36","doi-asserted-by":"crossref","unstructured":"Park D, Ramanan D (2015) Articulated pose estimation with tiny synthetic videos. In: Computer vision and pattern recognition workshops, pp 58\u201366","DOI":"10.1109\/CVPRW.2015.7301337"},{"key":"5086_CR37","doi-asserted-by":"crossref","unstructured":"Pavlakos G, Zhou X, Derpanis KG, Daniilidis K (2017) Coarse-to-fine volumetric prediction for single-image 3D human pose. In: Computer vision and pattern recognition, pp 1263\u20131272","DOI":"10.1109\/CVPR.2017.139"},{"key":"5086_CR38","doi-asserted-by":"crossref","unstructured":"Pishchulin L, Jain A, Andriluka M, Thorm\u00e4hlen T, Schiele B (2012) Articulated people detection and pose estimation: reshaping the future. In: Computer vision and pattern recognition, pp 3178\u20133185","DOI":"10.1109\/CVPR.2012.6248052"},{"key":"5086_CR39","doi-asserted-by":"crossref","unstructured":"Radwan I, Dhall A, Goecke R (2013) Monocular image 3D human pose estimation under self-occlusion. In: International conference on computer vision, pp 1888\u20131895","DOI":"10.1109\/ICCV.2013.237"},{"key":"5086_CR40","doi-asserted-by":"crossref","unstructured":"Ramakrishna V, Kanade T, Sheikh Y (2012) Reconstructing 3D human pose from 2D image landmarks. In: European conference on computer vision, pp 573\u2013586","DOI":"10.1007\/978-3-642-33765-9_41"},{"key":"5086_CR41","doi-asserted-by":"crossref","unstructured":"Sanzari M, Ntouskos V, Pirri F (2016) Bayesian image based 3D pose estimation. In: European conference on computer vision, pp 566\u2013582","DOI":"10.1007\/978-3-319-46484-8_34"},{"key":"5086_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2016.09.002","volume":"152","author":"N Sarafianos","year":"2016","unstructured":"Sarafianos N, Boteanu B, Ionescu B, Kakadiaris IA (2016) 3D human pose estimation: a review of the literature and analysis of covariates. Comput Vis Image Underst 152:1\u201320","journal-title":"Comput Vis Image Underst"},{"issue":"12","key":"5086_CR43","doi-asserted-by":"publisher","first-page":"3223","DOI":"10.1016\/j.patcog.2013.05.019","volume":"46","author":"S Sedai","year":"2013","unstructured":"Sedai S, Bennamoun M, Huynh DQ (2013) Discriminative fusion of shape and appearance features for human pose estimation. Pattern Recognit 46(12):3223\u20133237","journal-title":"Pattern Recognit"},{"key":"5086_CR44","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.patcog.2018.03.027","volume":"81","author":"Y Shao","year":"2018","unstructured":"Shao Y, Nong S, Gao C, Li M (2018) Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification. Pattern Recognit 81:102\u2013114","journal-title":"Pattern Recognit"},{"issue":"1\u20132","key":"5086_CR45","first-page":"4","volume":"87","author":"L Sigal","year":"2006","unstructured":"Sigal L, Black MJ (2006) Humaneva: synchronized video and motion capture dataset for evaluation of articulated human motion. Int J Comput Vis 87(1\u20132):4\u201327","journal-title":"Int J Comput Vis"},{"key":"5086_CR46","doi-asserted-by":"crossref","unstructured":"Sigal L, Memisevic R, Fleet DJ (2009) Shared kernel information embedding for discriminative inference. In: Computer vision and pattern recognition, pp 2852\u20132859","DOI":"10.1109\/CVPR.2009.5206576"},{"key":"5086_CR47","doi-asserted-by":"crossref","unstructured":"Simo-Serra E, Quattoni A, Torras C, Moreno-Noguer F (2013) A joint model for 2D and 3D pose estimation from a single image. In: Computer vision and pattern recognition, pp 3634\u20133641","DOI":"10.1109\/CVPR.2013.466"},{"key":"5086_CR48","doi-asserted-by":"crossref","unstructured":"Simo-Serra E, Ramisa A, Aleny\u00e0 G, Torras C (2012) Single image 3D human pose estimation from noisy observations. In: Computer vision and pattern recognition, pp 2673\u20132680","DOI":"10.1109\/CVPR.2012.6247988"},{"key":"5086_CR49","doi-asserted-by":"crossref","unstructured":"Sminchisescu C, Jepson A (2004) Generative modeling for continuous non-linearly embedded visual inference. In: International conference on machine learning","DOI":"10.1145\/1015330.1015371"},{"key":"5086_CR50","doi-asserted-by":"crossref","unstructured":"Tekin B, Katircioglu I, Salzmann M, Lepetit V, Fua P (2016) Structured prediction of 3D human pose with deep neural networks. arXiv:1605.05180","DOI":"10.5244\/C.30.130"},{"key":"5086_CR51","doi-asserted-by":"crossref","unstructured":"Tekin B, Rozantsev A, Lepetit V, Fua P (2016) Direct prediction of 3D body poses from motion compensated sequences. In: Computer vision and pattern recognition, pp 991\u20131000","DOI":"10.1109\/CVPR.2016.113"},{"key":"5086_CR52","doi-asserted-by":"crossref","unstructured":"Varol G, Romero J, Martin X, Mahmood N, Black M, Laptev I, Schmid C (2017) Learning from synthetic humans. In: Computer vision and pattern recognition, pp 4627\u20134635","DOI":"10.1109\/CVPR.2017.492"},{"issue":"5","key":"5086_CR53","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TPAMI.2018.2828427","volume":"41","author":"C Wang","year":"2019","unstructured":"Wang C, Wang Y, Lin Z, Yuille A (2019) Robust 3D human pose estimation from single images or video sequences. IEEE Trans Pattern Anal Mach Intell 41(5):1227\u20131241","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5086_CR54","doi-asserted-by":"crossref","unstructured":"Wang C, Wang Y, Lin Z, Yuille AL, Gao W (2014) Robust estimation of 3D human poses from a single image. In: Computer vision and pattern recognition, pp 2369\u20132376","DOI":"10.1109\/CVPR.2014.303"},{"key":"5086_CR55","doi-asserted-by":"crossref","unstructured":"Wang K, Lin L, Jiang C, Qian C, Wei P (2019) 3D human pose machines with self-supervised learning. In: IEEE transactions on pattern analysis and machine intelligence, p 1","DOI":"10.1109\/TPAMI.2019.2892452"},{"key":"5086_CR56","doi-asserted-by":"crossref","unstructured":"Wei SE, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines. In: Computer vision and pattern recognition, pp 4724\u20134732","DOI":"10.1109\/CVPR.2016.511"},{"issue":"10","key":"5086_CR57","doi-asserted-by":"publisher","first-page":"6643","DOI":"10.1007\/s00521-018-3497-y","volume":"31","author":"X Yang","year":"2019","unstructured":"Yang X, Sun Q, Wang T (2019) No-reference image quality assessment based on sparse representation. Neural Comput Appl 31(10):6643\u20136658","journal-title":"Neural Comput Appl"},{"key":"5086_CR58","doi-asserted-by":"crossref","unstructured":"Yang Y, Ramanan D (2011) Articulated pose estimation with flexible mixtures-of-parts. In: Computer vision and pattern recognition, pp 1385\u20131392","DOI":"10.1109\/CVPR.2011.5995741"},{"issue":"6","key":"5086_CR59","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1007\/s12559-018-9598-1","volume":"10","author":"Z Yang","year":"2018","unstructured":"Yang Z, Tang L, Zhang K, Wong PK (2018) Multi-view cnn feature aggregation with elm auto-encoder for 3d shape recognition. Cognit Comput 10(6):908\u2013921","journal-title":"Cognit Comput"},{"key":"5086_CR60","doi-asserted-by":"crossref","unstructured":"Yasin H, Iqbal U, Kr\u00fcger B, Weber A, Gall J (2016) A dual-source approach for 3D pose estimation from a single image. In: Computer vision and pattern recognition, pp 4948\u20134956","DOI":"10.1109\/CVPR.2016.535"},{"issue":"10","key":"5086_CR61","doi-asserted-by":"publisher","first-page":"2965","DOI":"10.1007\/s00521-017-2900-4","volume":"30","author":"S Zeng","year":"2018","unstructured":"Zeng S, Gou J, Yang X (2018) Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification. Neural Comput Appl 30(10):2965\u20132978","journal-title":"Neural Comput Appl"},{"key":"5086_CR62","unstructured":"Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: International conference on computer vision, pp 471\u2013478"},{"key":"5086_CR63","doi-asserted-by":"crossref","unstructured":"Zhou X, Huang Q, Sun X, Xue X, Wei Y (2017) Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: International conference on computer vision, pp 398\u2013407","DOI":"10.1109\/ICCV.2017.51"},{"key":"5086_CR64","doi-asserted-by":"crossref","unstructured":"Zhou X, Leonardos S, Hu X, Daniilidis K (2015) 3D shape estimation from 2d landmarks: a convex relaxation approach. In: Computer vision and pattern recognition, pp 4447\u20134455","DOI":"10.1109\/CVPR.2015.7299074"},{"key":"5086_CR65","doi-asserted-by":"crossref","unstructured":"Zhou X, Sun X, Zhang W, Liang S, Wei Y (2016) Deep kinematic pose regression. In: European conference on computer vision, pp 186\u2013201","DOI":"10.1007\/978-3-319-49409-8_17"},{"issue":"8","key":"5086_CR66","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1109\/TPAMI.2016.2605097","volume":"39","author":"X Zhou","year":"2017","unstructured":"Zhou X, Zhu M, Leonardos S, Daniilidis K (2017) Sparse representation for 3D shape estimation: a convex relaxation approach. IEEE Trans Pattern Anal Mach Intell 39(8):1648\u20131661","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5086_CR67","doi-asserted-by":"crossref","unstructured":"Zhou X, Zhu M, Leonardos S, Derpanis KG, Daniilidis K (2016) Sparseness meets deepness: 3D human pose estimation from monocular video. In: Computer vision and pattern recognition, pp 4966\u20134975","DOI":"10.1109\/CVPR.2016.537"},{"issue":"4","key":"5086_CR68","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1109\/TPAMI.2018.2816031","volume":"41","author":"X Zhou","year":"2019","unstructured":"Zhou X, Zhu M, Pavlakos G, Leonardos S, Derpanis KG, Daniilidis K (2019) Monocap: monocular human motion capture using a cnn coupled with a geometric prior. IEEE Trans Pattern Anal Mach Intell 41(4):901\u2013914","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05086-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05086-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05086-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T21:45:57Z","timestamp":1696283157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05086-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,24]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5086"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05086-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,6,24]]},"assertion":[{"value":"12 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}