{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:23:23Z","timestamp":1760711003484,"version":"3.37.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972035"],"award-info":[{"award-number":["61972035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["4222037"],"award-info":[{"award-number":["4222037"]}]},{"name":"Beijing Natural Science Foundation","award":["L181010"],"award-info":[{"award-number":["L181010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03714-x","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T17:02:37Z","timestamp":1654275757000},"page":"3864-3876","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Self-supervised method for 3D human pose estimation with consistent shape and viewpoint factorization"],"prefix":"10.1007","volume":"53","author":[{"given":"Zhichao","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3528-4739","authenticated-orcid":false,"given":"Kan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"key":"3714_CR1","doi-asserted-by":"publisher","unstructured":"Andriluka M, Pishchulin L, Gehler P et al (2014) 2D human pose estimation: new benchmark and state of the art analysis. In: Conference on computer vision and pattern recognition. IEEE, pp 3686\u20133693. https:\/\/doi.org\/10.1109\/cvpr.2014.471","DOI":"10.1109\/cvpr.2014.471"},{"key":"3714_CR2","doi-asserted-by":"publisher","unstructured":"Artacho B, Savakis A (2021) Unipose+: a unified framework for 2D and 3D human pose estimation in images and videos. IEEE Trans Pattern Anal Mach Intell, pp 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2021.3124736","DOI":"10.1109\/TPAMI.2021.3124736"},{"issue":"7","key":"3714_CR3","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TPAMI.2015.2487966","volume":"38","author":"C Bao","year":"2016","unstructured":"Bao C, Ji H, Quan Y et al (2016) Dictionary learning for sparse coding: algorithms and convergence analysis. IEEE Trans Pattern Anal Mach Intell 38(7):1356\u20131369. https:\/\/doi.org\/10.1109\/TPAMI.2015.2487966","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3714_CR4","doi-asserted-by":"publisher","unstructured":"Cai Y, Ge L, Liu J et al (2019) Exploiting spatial-temporal relationships for 3D pose estimation via graph convolutional networks. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 2272\u20132281. https:\/\/doi.org\/10.1109\/ICCV.2019.00236","DOI":"10.1109\/ICCV.2019.00236"},{"key":"3714_CR5","doi-asserted-by":"publisher","unstructured":"Chen CH, Ramanan D (2017) 3D human pose estimation=\u20092D pose estimation + matching. In: Conference on computer vision and pattern recognition (CVPR). IEEE, pp 5759\u20135767. https:\/\/doi.org\/10.1109\/cvpr.2017.610","DOI":"10.1109\/cvpr.2017.610"},{"key":"3714_CR6","doi-asserted-by":"publisher","unstructured":"Chen CH, Tyagi A, Agrawal A et al (2019) Unsupervised 3d pose estimation with geometric self-supervision. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 5707\u20135717. https:\/\/doi.org\/10.1109\/CVPR.2019.00586","DOI":"10.1109\/CVPR.2019.00586"},{"key":"3714_CR7","doi-asserted-by":"publisher","unstructured":"Chen X, Lin KY, Liu W et al (2019) Weakly-supervised discovery of geometry-aware representation for 3D human pose estimation. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 10,887\u201310,896. https:\/\/doi.org\/10.1109\/CVPR.2019.01115","DOI":"10.1109\/CVPR.2019.01115"},{"key":"3714_CR8","doi-asserted-by":"publisher","unstructured":"Chen Y, Wang Z, Peng Y et al (2018) Cascaded pyramid network for multi-person pose estimation. In: Conference on computer vision and pattern recognition. IEEE\/CVF, pp 7103\u20137112. https:\/\/doi.org\/10.1109\/CVPR.2018.00742","DOI":"10.1109\/CVPR.2018.00742"},{"key":"3714_CR9","doi-asserted-by":"publisher","unstructured":"Cheng Y, Wang B, Yang B et al (2021) Monocular 3D multi-person pose estimation by integrating top-down and bottom-up networks. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 7645\u20137655. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00756","DOI":"10.1109\/CVPR46437.2021.00756"},{"issue":"3","key":"3714_CR10","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1109\/TPAMI.2020.3019139","volume":"44","author":"H Ci","year":"2022","unstructured":"Ci H, Ma X, Wang C et al (2022) Locally connected network for monocular 3D human pose estimation. IEEE Trans Pattern Anal Mach Intell 44(3):1429\u20131442. https:\/\/doi.org\/10.1109\/TPAMI.2020.3019139","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3714_CR11","doi-asserted-by":"publisher","unstructured":"Dong J, Fang Q, Jiang W et al (2021) Fast and robust multi-person 3D pose estimation and tracking from multiple views. IEEE Trans Pattern Anal Mach Intell, pp 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2021.3098052","DOI":"10.1109\/TPAMI.2021.3098052"},{"key":"3714_CR12","doi-asserted-by":"publisher","unstructured":"Fabbri M, Lanzi F, Calderara S et al (2020) Compressed volumetric heatmaps for multi-person 3D pose estimation. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 7202\u20137211. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00723","DOI":"10.1109\/cvpr42600.2020.00723"},{"key":"3714_CR13","doi-asserted-by":"crossref","unstructured":"Fang H, Xu Y, Wang W et al (2018) Learning pose grammar to encode human body configuration for 3D pose estimation. In: Proceedings of the AAAI conference on artificial intelligence, pp 6821\u20136828","DOI":"10.1609\/aaai.v32i1.12270"},{"key":"3714_CR14","doi-asserted-by":"publisher","unstructured":"Habibie I, Xu W, Mehta D et al (2019) In the wild human pose estimation using explicit 2D features and intermediate 3D representations. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 10,897\u201310,906. https:\/\/doi.org\/10.1109\/CVPR.2019.01116","DOI":"10.1109\/CVPR.2019.01116"},{"key":"3714_CR15","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: International conference on computer vision (ICCV). IEEE, pp 1026\u20131034. https:\/\/doi.org\/10.1109\/ICCV.2015.123","DOI":"10.1109\/ICCV.2015.123"},{"issue":"7","key":"3714_CR16","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","unstructured":"Ionescu C, Papava D, Olaru V et al (2013) 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. https:\/\/doi.org\/10.1109\/TPAMI.2013.248","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3714_CR17","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cviu.2018.03.007","volume":"172","author":"U Iqbal","year":"2018","unstructured":"Iqbal U, Doering A, Yasin H et al (2018) A dual-source approach for 3D human pose estimation from single images. Comput Vis Image Underst 172:37\u201349. https:\/\/doi.org\/10.1016\/j.cviu.2018.03.007","journal-title":"Comput Vis Image Underst"},{"key":"3714_CR18","doi-asserted-by":"publisher","unstructured":"Iqbal U, Molchanov P, Kautz J (2020) Weakly-supervised 3D human pose learning via multi-view images in the wild. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 5242\u20135251. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00529","DOI":"10.1109\/CVPR42600.2020.00529"},{"key":"3714_CR19","doi-asserted-by":"publisher","unstructured":"Kanazawa A, Black MJ, Jacobs DW et al (2018) End-to-end recovery of human shape and pose. In: Conference on computer vision and pattern recognition. IEEE\/CVF, pp 7122\u20137131. https:\/\/doi.org\/10.1109\/CVPR.2018.00744","DOI":"10.1109\/CVPR.2018.00744"},{"key":"3714_CR20","doi-asserted-by":"publisher","unstructured":"Kocabas M, Karagoz S, Akbas E (2019) Self-supervised learning of 3D human pose using multi-view geometry. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 1077\u20131086. https:\/\/doi.org\/10.1109\/CVPR.2019.00117","DOI":"10.1109\/CVPR.2019.00117"},{"key":"3714_CR21","doi-asserted-by":"publisher","unstructured":"Kolotouros N, Pavlakos G, Black M et al (2019) Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 2252\u20132261. https:\/\/doi.org\/10.1109\/ICCV.2019.00234","DOI":"10.1109\/ICCV.2019.00234"},{"key":"3714_CR22","doi-asserted-by":"publisher","unstructured":"Kong C, Lucey S (2019) Deep interpretable non-rigid structure from motion. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 1558\u20131567. https:\/\/doi.org\/10.1109\/iccv.2019.00164","DOI":"10.1109\/iccv.2019.00164"},{"key":"3714_CR23","doi-asserted-by":"publisher","unstructured":"Kundu JN, Seth S, Jampani V et al (2020) Self-supervised 3D human pose estimation via part guided novel image synthesis. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 6151\u20136161. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00619","DOI":"10.1109\/CVPR42600.2020.00619"},{"key":"3714_CR24","doi-asserted-by":"publisher","unstructured":"Li S, Ke L, Pratama K et al (2020) Cascaded deep monocular 3D human pose estimation with evolutionary training data. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 6172\u20136182. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00621","DOI":"10.1109\/CVPR42600.2020.00621"},{"issue":"07","key":"3714_CR25","doi-asserted-by":"publisher","first-page":"11,442","DOI":"10.1609\/aaai.v34i07.6808","volume":"34","author":"Y Li","year":"2020","unstructured":"Li Y, Li K, Jiang S et al (2020) Geometry-driven self-supervised method for 3D human pose estimation. Proceedings of the AAAI Conference on Artificial Intelligence 34(07):11,442\u201311,449. https:\/\/doi.org\/10.1609\/aaai.v34i07.6808","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"12","key":"3714_CR26","doi-asserted-by":"publisher","first-page":"4229","DOI":"10.1109\/TPAMI.2020.2974454","volume":"43","author":"Z Li","year":"2021","unstructured":"Li Z, Dekel T, Cole F et al (2021) Mannequinchallenge: learning the depths of moving people by watching frozen people. IEEE Trans Pattern Anal Mach Intell 43 (12):4229\u20134241. https:\/\/doi.org\/10.1109\/TPAMI.2020.2974454","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3714_CR27","doi-asserted-by":"publisher","unstructured":"Lin J, Lee GH (2021) Multi-view multi-person 3D pose estimation with plane sweep stereo. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 11,881\u201311,890. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01171","DOI":"10.1109\/CVPR46437.2021.01171"},{"issue":"2","key":"3714_CR28","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TPAMI.2019.2894422","volume":"42","author":"J Liu","year":"2020","unstructured":"Liu J, Ding H, Shahroudy A et al (2020) Feature boosting network for 3D pose estimation. IEEE Trans Pattern Anal Mach Intell 42(2):494\u2013501. https:\/\/doi.org\/10.1109\/TPAMI.2019.2894422","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3714_CR29","doi-asserted-by":"publisher","unstructured":"Ma X, Su J, Wang C et al (2021) Context modeling in 3d human pose estimation: a unified perspective. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 6234\u20136243. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00617","DOI":"10.1109\/CVPR46437.2021.00617"},{"key":"3714_CR30","doi-asserted-by":"publisher","unstructured":"Martinez J, Hossain R, Romero J et al (2017) A simple yet effective baseline for 3d human pose estimation. In: International conference on computer vision (ICCV). IEEE, pp 2659\u20132668. https:\/\/doi.org\/10.1109\/ICCV.2017.288","DOI":"10.1109\/ICCV.2017.288"},{"key":"3714_CR31","doi-asserted-by":"publisher","unstructured":"Mehta D, Rhodin H, Casas D et al (2017) Monocular 3D human pose estimation in the wild using improved cnn supervision. In: International conference on 3d vision (3DV), pp 506\u2013516. https:\/\/doi.org\/10.1109\/3DV.2017.00064","DOI":"10.1109\/3DV.2017.00064"},{"key":"3714_CR32","doi-asserted-by":"publisher","unstructured":"Mitra R, Gundavarapu NB, Sharma A et al (2020) Multiview-consistent semi-supervised learning for 3d human pose estimation. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 6906\u20136915. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00694","DOI":"10.1109\/cvpr42600.2020.00694"},{"key":"3714_CR33","doi-asserted-by":"publisher","unstructured":"Novotny D, Ravi N, Graham B et al (2019) C3dpo: canonical 3d pose networks for non-rigid structure from motion. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 7687\u20137696. https:\/\/doi.org\/10.1109\/ICCV.2019.00778","DOI":"10.1109\/ICCV.2019.00778"},{"key":"3714_CR34","doi-asserted-by":"publisher","unstructured":"Pavlakos G, Zhou X, Derpanis KG et al (2017) Coarse-to-fine volumetric prediction for single-image 3D human pose. In: Conference on computer vision and pattern recognition (CVPR). IEEE, pp 1263\u20131272. https:\/\/doi.org\/10.1109\/CVPR.2017.139","DOI":"10.1109\/CVPR.2017.139"},{"key":"3714_CR35","doi-asserted-by":"publisher","unstructured":"Pavlakos G, Zhou X, Derpanis KG et al (2017) Harvesting multiple views for marker-less 3D human pose annotations. In: Conference on computer vision and pattern recognition (CVPR). IEEE, pp 1253\u20131262. https:\/\/doi.org\/10.1109\/CVPR.2017.138","DOI":"10.1109\/CVPR.2017.138"},{"key":"3714_CR36","doi-asserted-by":"publisher","unstructured":"Pavllo D, Feichtenhofer C, Grangier D et al (2019) 3D human pose estimation in video with temporal convolutions and semi-supervised training. In: conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 7745\u20137754. https:\/\/doi.org\/10.1109\/CVPR.2019.00794","DOI":"10.1109\/CVPR.2019.00794"},{"key":"3714_CR37","doi-asserted-by":"publisher","unstructured":"Rhodin H, Meyer F, Sporri J et al (2018) Learning monocular 3D human pose estimation from multi-view images. In: Conference on computer vision and pattern recognition. IEEE\/CVF, pp 8437\u20138446. https:\/\/doi.org\/10.1109\/CVPR.2018.00880","DOI":"10.1109\/CVPR.2018.00880"},{"key":"3714_CR38","doi-asserted-by":"publisher","unstructured":"Rhodin H, Salzmann M, Fua P (2018) Unsupervised geometry-aware representation for 3D human pose estimation. In: Computer vision ECCV 2018, pp 765\u2013782. https:\/\/doi.org\/10.1007\/978-3-030-01249-6_46","DOI":"10.1007\/978-3-030-01249-6_46"},{"key":"3714_CR39","doi-asserted-by":"publisher","first-page":"5944","DOI":"10.1109\/TIP.2021.3090531","volume":"30","author":"M Scetbon","year":"2021","unstructured":"Scetbon M, Elad M, Milanfar P (2021) Deep k-SVD denoising. IEEE Trans Image Process 30:5944\u20135955. https:\/\/doi.org\/10.1109\/tip.2021.3090531","journal-title":"IEEE Trans Image Process"},{"key":"3714_CR40","doi-asserted-by":"publisher","unstructured":"Sun X, Xiao B, Wei F et al (2018) Integral human pose regression. In: Computer vision ECCV 2018, pp 536\u2013553. https:\/\/doi.org\/10.1007\/978-3-030-01231-1_33","DOI":"10.1007\/978-3-030-01231-1_33"},{"key":"3714_CR41","doi-asserted-by":"publisher","unstructured":"Tekin B, Marquez-Neila P, Salzmann M et al (2017) Learning to fuse 2D and 3D image cues for monocular body pose estimation. In: International conference on computer vision (ICCV). IEEE, pp 3961\u20133970. https:\/\/doi.org\/10.1109\/ICCV.2017.425","DOI":"10.1109\/ICCV.2017.425"},{"key":"3714_CR42","doi-asserted-by":"publisher","unstructured":"Tome D, Alldieck T, Peluse P et al (2020) Selfpose: 3D egocentric pose estimation from a headset mounted camera. IEEE Trans Pattern Anal Mach Intell, pp 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2020.3029700","DOI":"10.1109\/TPAMI.2020.3029700"},{"key":"3714_CR43","doi-asserted-by":"publisher","unstructured":"Tung HYF, Harley AW, Seto W et al (2017) Adversarial inverse graphics networks: learning 2D-to-3D lifting and image-to-image translation from unpaired supervision. In: International conference on computer vision (ICCV). IEEE, pp 4364\u20134372. https:\/\/doi.org\/10.1109\/ICCV.2017.467","DOI":"10.1109\/ICCV.2017.467"},{"key":"3714_CR44","doi-asserted-by":"publisher","unstructured":"Wandt B, Rosenhahn B (2019) Repnet: weakly supervised training of an adversarial reprojection network for 3D human pose estimation. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 7774\u20137783. https:\/\/doi.org\/10.1109\/CVPR.2019.00797","DOI":"10.1109\/CVPR.2019.00797"},{"key":"3714_CR45","doi-asserted-by":"publisher","unstructured":"Wandt B, Rudolph M, Zell P et al (2021) CanonPose: self-supervised monocular 3D human pose estimation in the wild. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 13,289\u201313,299. https:\/\/doi.org\/10.1109\/cvpr46437.2021.01309","DOI":"10.1109\/cvpr46437.2021.01309"},{"key":"3714_CR46","doi-asserted-by":"publisher","unstructured":"Wang C, Kong C, Lucey S (2019) Distill knowledge from nrsfm for weakly supervised 3D pose learning. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 743\u2013752. https:\/\/doi.org\/10.1109\/ICCV.2019.00083","DOI":"10.1109\/ICCV.2019.00083"},{"issue":"01","key":"3714_CR47","doi-asserted-by":"publisher","first-page":"8925","DOI":"10.1609\/aaai.v33i01.33018925","volume":"33","author":"C Wang","year":"2019","unstructured":"Wang C, Qiu H, Yuille AL et al (2019) Learning basis representation to refine 3D human pose estimations. Proceedings of the AAAI Conference on Artificial Intelligence 33(01):8925\u20138932. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018925","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"5","key":"3714_CR48","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 et al (2019) Robust 3D human pose estimation from single images or video sequences. IEEE Trans Pattern Anal Mach Intell 41(5):1227\u20131241. https:\/\/doi.org\/10.1109\/TPAMI.2018.2828427","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"3714_CR49","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1109\/TPAMI.2019.2892452","volume":"42","author":"K Wang","year":"2020","unstructured":"Wang K, Lin L, Jiang C et al (2020) 3D human pose machines with self-supervised learning. IEEE IEEE Trans Pattern Anal Mach Intell 42(5):1069\u20131082. https:\/\/doi.org\/10.1109\/TPAMI.2019.2892452","journal-title":"IEEE IEEE Trans Pattern Anal Mach Intell"},{"key":"3714_CR50","doi-asserted-by":"publisher","unstructured":"Wehrbein T, Rudolph M, Rosenhahn B et al (2021) Probabilistic monocular 3D human pose estimation with normalizing flows. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 11,179\u201311,188. https:\/\/doi.org\/10.1109\/iccv48922.2021.01101","DOI":"10.1109\/iccv48922.2021.01101"},{"key":"3714_CR51","doi-asserted-by":"publisher","unstructured":"Xu Y, Wang W, Liu T et al (2021) Monocular 3d pose estimation via pose grammar and data augmentation. IEEE Trans Pattern Anal Mach Intell, pp 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2021.3087695","DOI":"10.1109\/TPAMI.2021.3087695"},{"key":"3714_CR52","doi-asserted-by":"publisher","unstructured":"Yang W, Ouyang W, Wang X et al (2018) 3D human pose estimation in the wild by adversarial learning. In: Conference on computer vision and pattern recognition. IEEE\/CVF, pp 5255\u20135264. https:\/\/doi.org\/10.1109\/CVPR.2018.00551","DOI":"10.1109\/CVPR.2018.00551"},{"key":"3714_CR53","doi-asserted-by":"publisher","unstructured":"Yuan Y, Wei SE, Simon T et al (2021) SimPoE: simulated character control for 3D human pose estimation. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 7155\u20137165. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00708","DOI":"10.1109\/CVPR46437.2021.00708"},{"key":"3714_CR54","doi-asserted-by":"publisher","unstructured":"Zhang Z, Wang C, Qin W et al (2020) Fusing wearable imus with multi-view images for human pose estimation: a geometric approach. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 2197\u20132206. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00227","DOI":"10.1109\/CVPR42600.2020.00227"},{"key":"3714_CR55","doi-asserted-by":"publisher","unstructured":"Zhang Z, Hu L, Deng X et al (2021) Sequential 3D human pose estimation using adaptive point cloud sampling strategy. In: Proceedings of the thirtieth international joint conference on artificial intelligence, pp 1330\u20131337. https:\/\/doi.org\/10.24963\/ijcai.2021\/184","DOI":"10.24963\/ijcai.2021\/184"},{"key":"3714_CR56","doi-asserted-by":"publisher","unstructured":"Zhao L, Peng X, Tian Y et al (2019) Semantic graph convolutional networks for 3D human pose regression. In: Conference on computer vision and pattern recognition (CVPR). IEEE\/CVF, pp 3420\u20133430. https:\/\/doi.org\/10.1109\/CVPR.2019.00354","DOI":"10.1109\/CVPR.2019.00354"},{"key":"3714_CR57","doi-asserted-by":"publisher","unstructured":"Zheng C, Zhu S, Mendieta M et al (2021) 3D human pose estimation with spatial and temporal transformers. In: International conference on computer vision (ICCV). IEEE\/CVF, pp 11,636\u201311,645. https:\/\/doi.org\/10.1109\/iccv48922.2021.01145","DOI":"10.1109\/iccv48922.2021.01145"},{"key":"3714_CR58","doi-asserted-by":"publisher","unstructured":"Zhou K, Han X, Jiang N et al (2021) HEMlets posh: learning part-centric heatmap triplets for 3D human pose and shape estimation. IEEE Trans Pattern Anal Mach Intell, pp 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2021.3051173","DOI":"10.1109\/TPAMI.2021.3051173"},{"key":"3714_CR59","doi-asserted-by":"publisher","unstructured":"Zhou X, Huang Q, Sun X et al (2017) Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: International conference on computer vision (ICCV). IEEE, pp 398\u2013407. https:\/\/doi.org\/10.1109\/iccv.2017.51","DOI":"10.1109\/iccv.2017.51"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03714-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03714-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03714-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T06:33:19Z","timestamp":1675233199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03714-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,3]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3714"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03714-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,6,3]]},"assertion":[{"value":"1 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}