{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:43:00Z","timestamp":1774370580378,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Key Program of NSFC","award":["U1908214"],"award-info":[{"award-number":["U1908214"]}]},{"name":"Special Project of Central Government Guiding Local Science and Technology Development","award":["2021JH6\/10500140"],"award-info":[{"award-number":["2021JH6\/10500140"]}]},{"name":"Program for the Liaoning Distinguished Professor"},{"name":"Program for Innovative Research Team in University of Liaoning Province, Dalian and Dalian University"},{"name":"Science and Technology Innovation Fund of Dalian","award":["2020JJ25CY001"],"award-info":[{"award-number":["2020JJ25CY001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10489-022-03312-x","type":"journal-article","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T21:02:27Z","timestamp":1647550947000},"page":"15690-15702","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["High-order local connection network for 3D human pose estimation based on GCN"],"prefix":"10.1007","volume":"52","author":[{"given":"Wei","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsheng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3489-6661","authenticated-orcid":false,"given":"Jing","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaopeng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"3312_CR1","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C (2020) X3d:Expanding architectures for efficient video recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 203\u2013213. arXiv:2004.04730","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"3312_CR2","doi-asserted-by":"publisher","unstructured":"Munro J, Damen D (2020) Multi-modal domain adaptation for fine-grained action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 122\u2013132. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00020","DOI":"10.1109\/CVPR42600.2020.00020"},{"key":"3312_CR3","doi-asserted-by":"publisher","unstructured":"Yang C, Xu Y, Shi J, Dai B, Zhou B (2020) Temporal pyramid network for action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 591\u2013600. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00067","DOI":"10.1109\/CVPR42600.2020.00067"},{"key":"3312_CR4","doi-asserted-by":"publisher","unstructured":"Porcheron M, Fischer J.E, Reeves S, Sharples S (2018) Voice interfaces in everyday life. In: Proceedings of the 2018 CHI conference on human factors in computing systems, pp 1\u201312. https:\/\/doi.org\/10.1145\/3X00000.1735743174214","DOI":"10.1145\/3X00000.1735743174214"},{"key":"3312_CR5","doi-asserted-by":"publisher","unstructured":"Wu S, Wang Z, Shen B, Wang J-H, Dongdong L (2020) Human-computer interaction based on machine vision of a smart assembly workbench. Assembly Automation. https:\/\/doi.org\/10.1108\/AA-10-2018-0170","DOI":"10.1108\/AA-10-2018-0170"},{"key":"3312_CR6","doi-asserted-by":"publisher","unstructured":"Pustejovsky J, Krishnaswamy N (2021) Embodied human computer interaction. KI-K\u00fcnstliche Intelligenz. https:\/\/doi.org\/10.1007\/s13218-021-00727-5","DOI":"10.1007\/s13218-021-00727-5"},{"key":"3312_CR7","doi-asserted-by":"crossref","unstructured":"Chan C, Ginosar S, Zhou T, Efros A.A (2019) Everybody dance now. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5933\u20135942. arXiv:1808.07371v2","DOI":"10.1109\/ICCV.2019.00603"},{"key":"3312_CR8","unstructured":"Ma L, Jia X, Sun Q, Schiele B, Tuytelaars T, Van Gool L (2017) Pose guided person image generation. In: Proceedings of the 31st international conference on neural information processing systems. NIPS\u201917. arXiv:1705.09368v1. Curran Associates Inc., Red Hook, pp 405\u2013415"},{"key":"3312_CR9","doi-asserted-by":"publisher","unstructured":"Siarohin A, Sangineto E, Lathuiliere S, Sebe N (2018) Deformable gans for pose-based human image generation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3408\u20133416. https:\/\/doi.org\/10.1109\/CVPR.2018.00359","DOI":"10.1109\/CVPR.2018.00359"},{"key":"3312_CR10","doi-asserted-by":"crossref","unstructured":"Moon G, Lee K.M (2020) I2l-meshnet:Image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single rgb image. In: Computer Vision\u2013ECCV 2020:16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VII 16, Springer, pp 752\u2013768. arXiv:2008.03713","DOI":"10.1007\/978-3-030-58571-6_44"},{"key":"3312_CR11","doi-asserted-by":"publisher","unstructured":"Pavlakos G, Zhou X, Daniilidis K (2018) Ordinal depth supervision for 3d human pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7307\u2013 7316. https:\/\/doi.org\/10.1109\/CVPR.2018.00763","DOI":"10.1109\/CVPR.2018.00763"},{"key":"3312_CR12","doi-asserted-by":"publisher","unstructured":"Pavlakos G, Zhou X, Derpanis K.G, Daniilidis K (2017) Coarse-to-fine volumetric prediction for single-image 3d human pose. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7025\u20137034. https:\/\/doi.org\/10.1109\/CVPR.2017.139","DOI":"10.1109\/CVPR.2017.139"},{"key":"3312_CR13","doi-asserted-by":"crossref","unstructured":"Li C, Lee G.H (2019) Generating multiple hypotheses for 3d human pose estimation with mixture density network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9887\u20139895. arXiv:1904.05547","DOI":"10.1109\/CVPR.2019.01012"},{"key":"3312_CR14","doi-asserted-by":"crossref","unstructured":"Wang M, Chen X, Liu W, Qian C, Lin L, Ma L (2018) Drpose3d:Depth ranking in 3d human pose estimation. In: Proceedings of the 27th international joint conference on artificial intelligence. IJCAI\u201918, pp 978\u2013984. arXiv:1805.08973","DOI":"10.24963\/ijcai.2018\/136"},{"key":"3312_CR15","doi-asserted-by":"publisher","unstructured":"Martinez J, Hossain R, Romero J, Little J.J (2017) A simple yet effective baseline for 3d human pose estimation. In: Proceedings of the IEEE international conference on computer vision, pp 2640\u20132649. https:\/\/doi.org\/10.1109\/ICCV.2017.288","DOI":"10.1109\/ICCV.2017.288"},{"key":"3312_CR16","doi-asserted-by":"crossref","unstructured":"Tekin B, M\u00e1rquez-Neila P, Salzmann M, Fua P (2017) Learning to fuse 2d and 3d image cues for monocular body pose estimation. In: Proceedings of the IEEE international conference on computer vision, pp 3941\u20133950. arXiv:1611.05708","DOI":"10.1109\/ICCV.2017.425"},{"key":"3312_CR17","doi-asserted-by":"publisher","unstructured":"Zhou K, Han X, Jiang N, Jia K, Lu J (2019) Hemlets pose:Learning part-centric heatmap triplets for accurate 3d human pose estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2344\u20132353. https:\/\/doi.org\/10.1109\/ICCV.2019.00243","DOI":"10.1109\/ICCV.2019.00243"},{"key":"3312_CR18","doi-asserted-by":"publisher","first-page":"107405","DOI":"10.1016\/j.asoc.2021.107405","volume":"108","author":"Y Wu","year":"2021","unstructured":"Wu Y, Jiang X, Fang Z, Gao Y, Fujita H (2021) Multi-modal 3d object detection by 2d-guided precision anchor proposal and multi-layer fusion. Appl Soft Comput 108:107405. https:\/\/doi.org\/10.1016\/j.asoc.2021.107405","journal-title":"Appl Soft Comput"},{"key":"3312_CR19","doi-asserted-by":"publisher","unstructured":"Xiao J, Li H, Qu G, Fujita H, Cao Y, Zhu J, Huang C (2021) Hope:heatmap and offset for pose estimation. Journal of Ambient Intelligence and Humanized Computing, pp 1\u201313. https:\/\/doi.org\/10.1007\/s12652-021-03124-w","DOI":"10.1007\/s12652-021-03124-w"},{"key":"3312_CR20","unstructured":"Kipf T.N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. arXiv:1609.02907"},{"key":"3312_CR21","doi-asserted-by":"publisher","unstructured":"Ci H, Wang C, Ma X, Wang Y (2019) Optimizing network structure for 3d human pose estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2262\u20132271. https:\/\/doi.org\/10.1109\/ICCV.2019.00235","DOI":"10.1109\/ICCV.2019.00235"},{"key":"3312_CR22","doi-asserted-by":"publisher","unstructured":"Liu K, Ding R, Zou Z, Wang L, Tang W (2020) A comprehensive study of weight sharing in graph networks for 3d human pose estimation. In: European conference on computer vision, Springer, pp 318\u2013334. https:\/\/doi.org\/10.1007\/978-3-030-58607-2_19","DOI":"10.1007\/978-3-030-58607-2_19"},{"key":"3312_CR23","doi-asserted-by":"publisher","unstructured":"Zhao L, Peng X, Tian Y, Kapadia M, Metaxas D.N (2019) Semantic graph convolutional networks for 3d human pose regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3425\u20133435. https:\/\/doi.org\/10.1109\/CVPR.2019.00354","DOI":"10.1109\/CVPR.2019.00354"},{"key":"3312_CR24","doi-asserted-by":"crossref","unstructured":"Xu T, Takano W (2021) Graph stacked hourglass networks for 3d human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16105\u201316114. arXiv:2103.16385","DOI":"10.1109\/CVPR46437.2021.01584"},{"key":"3312_CR25","doi-asserted-by":"crossref","unstructured":"Liu K, Zou Z, Tang W (2020) Learning global pose features in graph convolutional networks for 3d human pose estimation. In: Proceedings of the Asian conference on computer vision. https:\/\/accv2020.github.io\/miniconf\/poster_167.html","DOI":"10.1007\/978-3-030-69525-5_6"},{"key":"3312_CR26","doi-asserted-by":"publisher","unstructured":"Liu J, Rojas J, Li Y, Liang Z, Guan Y, Xi N, Zhu H (2021) A graph attention spatio-temporal convolutional network for 3d human pose estimation in video. In: 2021 IEEE international conference on robotics and automation (ICRA), IEEE, pp 3374\u20133380. https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561605","DOI":"10.1109\/ICRA48506.2021.9561605"},{"key":"3312_CR27","doi-asserted-by":"publisher","unstructured":"Cai Y, Ge L, Liu J, Cai J, Cham T.-J, Yuan J, Thalmann NM (2019) Exploiting spatial-temporal relationships for 3d pose estimation via graph convolutional networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2272\u20132281. https:\/\/doi.org\/10.1109\/ICCV.2019.00236","DOI":"10.1109\/ICCV.2019.00236"},{"issue":"7","key":"3312_CR28","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, Sminchisescu C (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":"3312_CR29","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. In: International conference on learning representations (ICLR2014), CBLS, April 2014. arXiv:1312.6203"},{"key":"3312_CR30","unstructured":"Xu B, Shen H, Cao Q, Qiu Y, Cheng X (2019) Graph wavelet neural network. In: International conference on learning representations. arXiv:1904.07785v1"},{"key":"3312_CR31","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems, vol 29. arXiv:1606.09375v2"},{"key":"3312_CR32","doi-asserted-by":"crossref","unstructured":"Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein M.M (2017) Geometric deep learning on graphs and manifolds using mixture model cnns. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5115\u20135124. arXiv:1611.08402","DOI":"10.1109\/CVPR.2017.576"},{"key":"3312_CR33","doi-asserted-by":"publisher","unstructured":"Gilmer J, Schoenholz S.S, Riley P.F, Vinyals O, Dahl G.E (2017) Neural message passing for quantum chemistry. In: International conference on machine learning, PMLR, pp 1263\u20131272. https:\/\/doi.org\/10.5555\/3305381.3305512","DOI":"10.5555\/3305381.3305512"},{"key":"3312_CR34","doi-asserted-by":"publisher","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. In: 2017 international conference on 3D vision (3DV), IEEE, pp 506\u2013516. https:\/\/doi.org\/10.1109\/3DV.2017.00064","DOI":"10.1109\/3DV.2017.00064"},{"key":"3312_CR35","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, Springer, pp 483\u2013499. arXiv:1603.06937","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"3312_CR36","doi-asserted-by":"publisher","unstructured":"Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2d human pose estimation:New benchmark and state of the art analysis. In: IEEE Conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2014.471","DOI":"10.1109\/CVPR.2014.471"},{"key":"3312_CR37","doi-asserted-by":"publisher","unstructured":"Pavllo D, Feichtenhofer C, Grangier D, Auli M (2019) 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7753\u20137762. https:\/\/doi.org\/10.1109\/CVPR.2019.00794","DOI":"10.1109\/CVPR.2019.00794"},{"key":"3312_CR38","unstructured":"Kingma D.P, Ba J (2015) 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings Bengio Y, LeCun Y (eds). arXiv:1412.6980"},{"key":"3312_CR39","doi-asserted-by":"crossref","unstructured":"Luvizon DC, Picard D, Tabia H (2018) 2d\/3d pose estimation and action recognition using multitask deep learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5137\u20135146. arXiv:1802.09232","DOI":"10.1109\/CVPR.2018.00539"},{"key":"3312_CR40","doi-asserted-by":"crossref","unstructured":"Sharma S, Varigonda PT, Bindal P, Sharma A, Jain A (2019) Monocular 3d human pose estimation by generation and ordinal ranking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2325\u20132334. arXiv:1904.01324","DOI":"10.1109\/ICCV.2019.00241"},{"key":"3312_CR41","doi-asserted-by":"crossref","unstructured":"Wang J, Huang S, Wang X, Tao D (2019) Not all parts are created equal:3d pose estimation by modeling bi-directional dependencies of body parts. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7771\u20137780. arXiv:1905.07862","DOI":"10.1109\/ICCV.2019.00786"},{"key":"3312_CR42","doi-asserted-by":"crossref","unstructured":"Zou Z, Liu K, 0003 LW, Tang W (2020) High-order graph convolutional networks for 3d human pose estimation. In: BMVC. https:\/\/www.evl.uic.edu\/pubs\/2518","DOI":"10.1109\/FG52635.2021.9667049"},{"key":"3312_CR43","doi-asserted-by":"crossref","unstructured":"Fang H-S, Xu Y, Wang W, Liu X, Zhu S-C (2018) Learning pose grammar to encode human body configuration for 3d pose estimation. In: Proceedings of the AAAI conference on artificial intelligence, vol 32. arXiv:1710.06513","DOI":"10.1609\/aaai.v32i1.12270"},{"key":"3312_CR44","doi-asserted-by":"publisher","unstructured":"Yang W, Ouyang W, Wang X, Ren J, Li H, Wang X (2018) 3d human pose estimation in the wild by adversarial learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5255\u20135264. https:\/\/doi.org\/10.1109\/CVPR.2018.00551","DOI":"10.1109\/CVPR.2018.00551"},{"key":"3312_CR45","doi-asserted-by":"publisher","unstructured":"Ci H, Ma X, Wang C, Wang Y. (2020) Locally connected network for monocular 3d human pose estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2020.3019139","DOI":"10.1109\/TPAMI.2020.3019139"},{"key":"3312_CR46","doi-asserted-by":"publisher","unstructured":"Johnson S, Everingham M (2010) Clustered pose and nonlinear appearance models for human pose estimation. In: Bmvc, vol 2, pp 5. https:\/\/doi.org\/10.5244\/C.24.12. Citeseer","DOI":"10.5244\/C.24.12"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03312-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03312-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03312-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T10:08:19Z","timestamp":1664618899000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03312-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,17]]},"references-count":46,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["3312"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03312-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,17]]},"assertion":[{"value":"28 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}