{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T04:29:06Z","timestamp":1785558546339,"version":"3.56.0"},"reference-count":251,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In recent years, human pose estimation has been widely studied as a branch task of computer vision. Human pose estimation plays an important role in the development of medicine, fitness, virtual reality, and other fields. Early human pose estimation technology used traditional manual modeling methods. Recently, human pose estimation technology has developed rapidly using deep learning. This study not only reviews the basic research of human pose estimation but also summarizes the latest cutting-edge technologies. In addition to systematically summarizing the human pose estimation technology, this article also extends to the upstream and downstream tasks of human pose estimation, which shows the positioning of human pose estimation technology more intuitively. In particular, considering the issues regarding computer resources and challenges concerning model performance faced by human pose estimation, the lightweight human pose estimation models and the transformer-based human pose estimation models are summarized in this paper. In general, this article classifies human pose estimation technology around types of methods, 2D or 3D representation of outputs, the number of people, views, and temporal information. Meanwhile, classic datasets and targeted datasets are mentioned in this paper, as well as metrics applied to these datasets. Finally, we generalize the current challenges and possible development of human pose estimation technology in the future.<\/jats:p>","DOI":"10.1007\/s10462-024-11060-2","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T04:19:44Z","timestamp":1736137184000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A systematic survey on human pose estimation: upstream and downstream tasks, approaches, lightweight models, and prospects"],"prefix":"10.1007","volume":"58","author":[{"given":"Zheyan","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingxiaofei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"issue":"18","key":"11060_CR1","doi-asserted-by":"crossref","first-page":"6821","DOI":"10.3390\/s22186821","volume":"22","author":"E Aidoo","year":"2022","unstructured":"Aidoo E, Wang X, Liu Z, Tenagyei EK, Owusu-Agyemang K, Kodjiku SL, Ejianya VN, Aggrey ESE (2022) Cofopose: conditional 2d pose estimation with transformers. Sensors 22(18):6821","journal-title":"Sensors"},{"key":"11060_CR2","doi-asserted-by":"publisher","unstructured":"Amin S, Andriluka M, Rohrbach M, Schiele B (2013) Multi-view pictorial structures for 3d human pose estimation. In: 24th British machine vision conference, https:\/\/doi.org\/10.5244\/c.27.45","DOI":"10.5244\/c.27.45"},{"key":"11060_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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3686\u20133693","DOI":"10.1109\/CVPR.2014.471"},{"key":"11060_CR4","doi-asserted-by":"crossref","unstructured":"Andriluka M, Iqbal U, Insafutdinov E, Pishchulin L, Milan A, Gall J, Schiele B (2018) Posetrack: a benchmark for human pose estimation and tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5167\u20135176","DOI":"10.1109\/CVPR.2018.00542"},{"key":"11060_CR5","doi-asserted-by":"crossref","unstructured":"Anvari T, Park K (2022) 3d human body pose estimation in virtual reality: a survey. In: 2022 13th International conference on information and communication technology convergence (ICTC), IEEE, pp 624\u2013628","DOI":"10.1109\/ICTC55196.2022.9952586"},{"key":"11060_CR6","doi-asserted-by":"crossref","unstructured":"Azizi N, Possegger H, Rodol\u00e0 E, Bischof H (2022) 3d human pose estimation using m\u00f6bius graph convolutional networks. In: European conference on computer vision, Springer, pp 160\u2013178","DOI":"10.1007\/978-3-031-19769-7_10"},{"issue":"10","key":"11060_CR7","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1109\/tpami.2015.2509986","volume":"38","author":"V Belagiannis","year":"2016","unstructured":"Belagiannis V, Amin S, Andriluka M, Schiele B, Navab N, Ilic S (2016) 3d pictorial structures revisited: multiple human pose estimation. IEEE Trans Pattern Anal Mach Intell 38(10):1929\u20131942. https:\/\/doi.org\/10.1109\/tpami.2015.2509986","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11060_CR8","doi-asserted-by":"crossref","first-page":"103655","DOI":"10.1016\/j.cviu.2023.103655","volume":"229","author":"C Bian","year":"2023","unstructured":"Bian C, Feng W, Meng F, Wang S (2023) Global-local contrastive multiview representation learning for skeleton-based action recognition. Comput Vis Image Underst 229:103655","journal-title":"Comput Vis Image Underst"},{"key":"11060_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107410","author":"Y Bin","year":"2020","unstructured":"Bin Y, Chen ZM, Wei XS, Chen X, Gao C, Sang N (2020) Structure-aware human pose estimation with graph convolutional networks. Pattern Recognit. https:\/\/doi.org\/10.1016\/j.patcog.2020.107410","journal-title":"Pattern Recognit"},{"key":"11060_CR10","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv: org\/2004.10934"},{"key":"11060_CR11","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: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part V 14, Springer, pp 561\u2013578","DOI":"10.1007\/978-3-319-46454-1_34"},{"key":"11060_CR12","doi-asserted-by":"crossref","unstructured":"Brandizzi N, Fanti A, Gallotta R, Russo S, Iocchi L, Nardi D, Napoli C (2022) Unsupervised pose estimation by means of an innovative vision transformer. In: International conference on artificial intelligence and soft computing, Springer, pp 3\u201320","DOI":"10.1007\/978-3-031-23480-4_1"},{"key":"11060_CR13","doi-asserted-by":"crossref","unstructured":"Bulat A, Kossaifi J, Tzimiropoulos G, Pantic M (2020) Toward fast and accurate human pose estimation via soft-gated skip connections. In: 2020 15th IEEE international conference on automatic face and gesture recognition (FG 2020), IEEE, pp 8\u201315","DOI":"10.1109\/FG47880.2020.00014"},{"key":"11060_CR14","doi-asserted-by":"crossref","unstructured":"Cai Y, Ge L, Liu J, Cai J, Cham TJ, 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","DOI":"10.1109\/ICCV.2019.00236"},{"key":"11060_CR15","doi-asserted-by":"crossref","unstructured":"Cai Y, Wang Z, Luo Z, Yin B, Du A, Wang H, Zhang X, Zhou X, Zhou E, Sun J (2020) Learning delicate local representations for multi-person pose estimation. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, Springer, pp 455\u2013472","DOI":"10.1007\/978-3-030-58580-8_27"},{"key":"11060_CR16","doi-asserted-by":"crossref","unstructured":"Cao X, Li X, Ma L, Huang Y, Feng X, Chen Z, Zeng H, Cao J (2022) Aggpose: deep aggregation vision transformer for infant pose estimation. arXiv: org\/2205.05277","DOI":"10.24963\/ijcai.2022\/700"},{"key":"11060_CR17","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291\u20137299","DOI":"10.1109\/CVPR.2017.143"},{"issue":"1","key":"11060_CR18","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/tpami.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y (2021) Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43(1):172\u2013186. https:\/\/doi.org\/10.1109\/tpami.2019.2929257","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11060_CR19","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision, Springer, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"11060_CR20","doi-asserted-by":"crossref","unstructured":"Carreira J, Sminchisescu C (2010) Constrained parametric min-cuts for automatic object segmentation. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 3241\u20133248","DOI":"10.1109\/CVPR.2010.5540063"},{"key":"11060_CR21","doi-asserted-by":"crossref","unstructured":"Carreira J, Agrawal P, Fragkiadaki K, Malik J (2016) Human pose estimation with iterative error feedback. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4733\u20134742","DOI":"10.1109\/CVPR.2016.512"},{"key":"11060_CR22","doi-asserted-by":"crossref","unstructured":"Chang S, Yuan L, Nie X, Huang Z, Zhou Y, Chen Y, Feng J, Yan S (2020) Towards accurate human pose estimation in videos of crowded scenes. In: Proceedings of the 28th ACM international conference on multimedia, pp 4630\u20134634","DOI":"10.1145\/3394171.3416299"},{"key":"11060_CR23","doi-asserted-by":"crossref","unstructured":"Chen B, Zhang H, Sun X, Duan D (2022a) Intelligent fitness system design based on esp32 and human posture recognition. In: Proceedings of the 2022 4th international conference on robotics, intelligent control and artificial intelligence, pp 642\u2013646","DOI":"10.1145\/3584376.3584489"},{"key":"11060_CR24","doi-asserted-by":"crossref","unstructured":"Chen L, Zhou D, Liu R, Zhang Q (2022b) Samkr: Bottom-up keypoint regression pose estimation method based on subspace attention module. In: 2022 International joint conference on neural networks (IJCNN), IEEE, pp 1\u20139","DOI":"10.1109\/IJCNN55064.2022.9891995"},{"key":"11060_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109691","author":"S Chen","year":"2022","unstructured":"Chen S, Xu Y, Pu Z, Ouyang J, Zou B (2022) Skeletonpose: exploiting human skeleton constraint for 3d human pose estimation. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2022.109691","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"11060_CR26","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/tcsvt.2021.3057267","volume":"32","author":"T Chen","year":"2022","unstructured":"Chen T, Fang C, Shen X, Zhu Y, Chen Z, Luo J (2022) Anatomy-aware 3d human pose estimation with bone-based pose decomposition. IEEE Trans Circuits Syst Video Technol 32(1):198\u2013209. https:\/\/doi.org\/10.1109\/tcsvt.2021.3057267","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11060_CR27","unstructured":"Chen X, Yuille A (2014) Articulated pose estimation by a graphical model with image dependent pairwise relations. In: 28th Conference on neural information processing systems (NIPS), Advances in Neural Information Processing Systems, vol\u00a027. Curran Associates, Inc"},{"key":"11060_CR28","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang Z, Peng Y, Zhang Z, Yu G, Sun J (2018) Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7103\u20137112","DOI":"10.1109\/CVPR.2018.00742"},{"issue":"22","key":"11060_CR29","doi-asserted-by":"crossref","first-page":"26568","DOI":"10.1007\/s10489-023-04805-z","volume":"53","author":"Y Chen","year":"2023","unstructured":"Chen Y, Gu R, Huang O, Jia G (2023) Vtp: volumetric transformer for multi-view multi-person 3d pose estimation. Appl Intell 53(22):26568\u201326579","journal-title":"Appl Intell"},{"key":"11060_CR30","doi-asserted-by":"crossref","unstructured":"Cheng B, Xiao B, Wang J, Shi H, Huang TS, Zhang L (2020) Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5386\u20135395","DOI":"10.1109\/CVPR42600.2020.00543"},{"key":"11060_CR31","doi-asserted-by":"crossref","unstructured":"Cheng Y, Yi P, Liu R, Dong J, Zhou D, Zhang Q (2021) Human-robot interaction method combining human pose estimation and motion intention recognition. In: 2021 IEEE 24th international conference on computer supported cooperative work in design (CSCWD), IEEE, pp 958\u2013963","DOI":"10.1109\/CSCWD49262.2021.9437772"},{"key":"11060_CR32","doi-asserted-by":"crossref","unstructured":"Chu X, Yang W, Ouyang W, Ma C, Yuille AL, Wang X (2017) Multi-context attention for human pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1831\u20131840","DOI":"10.1109\/CVPR.2017.601"},{"issue":"3","key":"11060_CR33","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1109\/tpami.2020.3019139","volume":"44","author":"H Ci","year":"2022","unstructured":"Ci H, Ma XX, Wang CY, Wang YZ (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":"11060_CR34","doi-asserted-by":"publisher","first-page":"3973","DOI":"10.1109\/tip.2022.3177959","volume":"31","author":"Y Dang","year":"2022","unstructured":"Dang Y, Yin J, Zhang S (2022) Relation-based associative joint location for human pose estimation in videos. IEEE Trans Image Process 31:3973\u20133986. https:\/\/doi.org\/10.1109\/tip.2022.3177959","journal-title":"IEEE Trans Image Process"},{"key":"11060_CR35","doi-asserted-by":"crossref","unstructured":"Debnath B, O\u2019brien M, Yamaguchi M, Behera A (2018) Adapting mobilenets for mobile based upper body pose estimation. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, pp 1\u20136","DOI":"10.1109\/AVSS.2018.8639378"},{"key":"11060_CR36","doi-asserted-by":"publisher","DOI":"10.1145\/3490235","author":"CFG Dos Santos","year":"2023","unstructured":"Dos Santos CFG, Oliveira DD, Passos LA et al (2023) Gait recognition based on deep learning: a survey. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3490235","journal-title":"ACM Comput Surv"},{"issue":"4","key":"11060_CR37","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.1109\/tcsvt.2022.3215564","volume":"33","author":"C Du","year":"2023","unstructured":"Du C, Yna Z, Yu H, Yu L, Xiong Z (2023) Hierarchical associative encoding and decoding for bottom-up human pose estimation. IEEE Trans Circuits Syst Video Technol 33(4):1762\u20131775. https:\/\/doi.org\/10.1109\/tcsvt.2022.3215564","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"11060_CR38","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s00530-022-00980-0","volume":"29","author":"S Dubey","year":"2023","unstructured":"Dubey S, Dixit M (2023) A comprehensive survey on human pose estimation approaches. Multimed Syst 29(1):167\u2013195. https:\/\/doi.org\/10.1007\/s00530-022-00980-0","journal-title":"Multimed Syst"},{"key":"11060_CR39","doi-asserted-by":"crossref","unstructured":"Eichner M, Ferrari V (2010) We are family: joint pose estimation of multiple persons. In: European conference on computer vision, Springer, pp 228\u2013242","DOI":"10.1007\/978-3-642-15549-9_17"},{"key":"11060_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/s22114109","author":"A El Kaid","year":"2022","unstructured":"El Kaid A, Brazey D, Barra V, Ba\u00efna K (2022) Top-down system for multi-person 3d absolute pose estimation from monocular videos. Sensors. https:\/\/doi.org\/10.3390\/s22114109","journal-title":"Sensors"},{"key":"11060_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104111","author":"A Elaanba","year":"2023","unstructured":"Elaanba A, Ridouani M, Hassouni L (2023) A stacked generalization chest-x-ray-based framework for mispositioned medical tubes and catheters detection. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2022.104111","journal-title":"Biomed Signal Process Control"},{"key":"11060_CR42","doi-asserted-by":"crossref","unstructured":"Fabbri M, Lanzi F, Calderara S, Palazzi A, Vezzani R, Cucchiara R (2018) Learning to detect and track visible and occluded body joints in a virtual world. In: Proceedings of the European conference on computer vision, pp 430\u2013446","DOI":"10.1007\/978-3-030-01225-0_27"},{"issue":"1","key":"11060_CR43","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1023\/b:Visi.0000042934.15159.49","volume":"61","author":"PF Felzenszwalb","year":"2005","unstructured":"Felzenszwalb PF, Huttenlocher DP (2005) Pictorial structures for object recognition. Int J Comput Vis 61(1):55\u201379. https:\/\/doi.org\/10.1023\/b:Visi.0000042934.15159.49","journal-title":"Int J Comput Vis"},{"key":"11060_CR44","doi-asserted-by":"crossref","unstructured":"Feng R, Gao Y, Ma X, Tse THE, Chang HJ (2023) Mutual information-based temporal difference learning for human pose estimation in video. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 17131\u201317141","DOI":"10.1109\/CVPR52729.2023.01643"},{"key":"11060_CR45","doi-asserted-by":"crossref","unstructured":"Ferrari V, Marin-Jimenez M, Zisserman A (2008) Progressive search space reduction for human pose estimation. In: 2008 IEEE conference on computer vision and pattern recognition, IEEE, pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587468"},{"issue":"1","key":"11060_CR46","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/T-C.1973.223602","volume":"100","author":"MA Fischler","year":"1973","unstructured":"Fischler MA, Elschlager RA (1973) The representation and matching of pictorial structures. IEEE Trans Comput 100(1):67\u201392","journal-title":"IEEE Trans Comput"},{"key":"11060_CR47","unstructured":"Fu CY, Liu W, Ranga A, Tyagi A, Berg AC (2017) Dssd: deconvolutional single shot detector. arXiv: org\/1701.06659"},{"key":"11060_CR48","doi-asserted-by":"publisher","first-page":"104282","DOI":"10.1016\/j.imavis.2021.104282","volume":"114","author":"MB Gamra","year":"2021","unstructured":"Gamra MB, Akhloufi MA (2021) A review of deep learning techniques for 2d and 3d human pose estimation. Image Vis Comput 114:104282. https:\/\/doi.org\/10.1016\/j.imavis.2021.104282","journal-title":"Image Vis Comput"},{"key":"11060_CR49","doi-asserted-by":"crossref","unstructured":"Ganapathi V, Plagemann C, Koller D, Thrun S (2010) Real time motion capture using a single time-of-flight camera. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 755\u2013762","DOI":"10.1109\/CVPR.2010.5540141"},{"issue":"1","key":"11060_CR50","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/thms.2022.3206663","volume":"53","author":"Q Gao","year":"2023","unstructured":"Gao Q, Ju ZJ, Chen YQ, Wang QW, Chi CL (2023) An efficient rgb-d hand gesture detection framework for dexterous robot hand-arm teleoperation system. IEEE T Hum-Mach Syst 53(1):13\u201323. https:\/\/doi.org\/10.1109\/thms.2022.3206663","journal-title":"IEEE T Hum-Mach Syst"},{"key":"11060_CR51","doi-asserted-by":"publisher","DOI":"10.3390\/s19224943","author":"M Garcia-Salguero","year":"2019","unstructured":"Garcia-Salguero M, Gonzalez-Jimenez J, Moreno FA (2019) Human 3d pose estimation with a tilting camera for social mobile robot interaction. Sensors. https:\/\/doi.org\/10.3390\/s19224943","journal-title":"Sensors"},{"key":"11060_CR52","doi-asserted-by":"crossref","unstructured":"Ghorbani S, Mahdaviani K, Thaler A, Kording K, Cook DJ, Blohm G, Troje NF (2020) Movi: A large multipurpose motion and video dataset. arXiv: org\/2003.01888","DOI":"10.1371\/journal.pone.0253157"},{"key":"11060_CR53","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"11060_CR54","doi-asserted-by":"crossref","unstructured":"Golda T, Kalb T, Schumann A, Beyerer J (2019) Human pose estimation for real-world crowded scenarios. In: 2019 16th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, pp 1\u20138","DOI":"10.1109\/AVSS.2019.8909823"},{"key":"11060_CR55","unstructured":"Gong X, Chen W, Jiang Y, Yuan Y, Liu X, Zhang Q, Li Y, Wang Z (2020) Autopose: searching multi-scale branch aggregation for pose estimation. arXiv: org\/2008.07018"},{"key":"11060_CR56","doi-asserted-by":"crossref","unstructured":"Graves A, Graves A (2012) Long short-term memory. Supervised sequence labelling with recurrent neural networks pp 37\u201345","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"11060_CR57","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2023.3264742","author":"K Gu","year":"2023","unstructured":"Gu K, Yang L, Mi MB, Yao A (2023) Bias-compensated integral regression for human pose estimation. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/tpami.2023.3264742","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"23","key":"11060_CR58","doi-asserted-by":"publisher","first-page":"32883","DOI":"10.1007\/s11042-022-13079-5","volume":"81","author":"R Gu","year":"2022","unstructured":"Gu R, Jiang Z, Wang G, McQuade K, Hwang JN (2022) Unsupervised universal hierarchical multi-person 3d pose estimation for natural scenes. Multimed Tools Appl 81(23):32883\u201332906. https:\/\/doi.org\/10.1007\/s11042-022-13079-5","journal-title":"Multimed Tools Appl"},{"key":"11060_CR59","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. Adv Neural Inform Process Syst 30"},{"key":"11060_CR60","doi-asserted-by":"crossref","unstructured":"Guo X, Dai Y (2018) Occluded joints recovery in 3d human pose estimation based on distance matrix. In: International conference on pattern recognition, IEEE, pp 1325\u20131330","DOI":"10.1109\/ICPR.2018.8545226"},{"issue":"6","key":"11060_CR61","doi-asserted-by":"publisher","first-page":"4755","DOI":"10.1007\/s10462-021-10116-x","volume":"55","author":"N Gupta","year":"2022","unstructured":"Gupta N, Gupta SK, Pathak RK, Jain V, Rashidi P, Suri JS (2022) Human activity recognition in artificial intelligence framework: a narrative review. Artif Intell Rev 55(6):4755\u20134808. https:\/\/doi.org\/10.1007\/s10462-021-10116-x","journal-title":"Artif Intell Rev"},{"key":"11060_CR62","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"11060_CR63","doi-asserted-by":"publisher","unstructured":"Hesse N, Bodensteiner C, Arens M, Hofmann UG, Weinberger R, Schroeder AS (2019) Computer vision for medical infant motion analysis: state of the art and rgb-d data set. In: 15th European conference on computer vision (ECCV), pp 32\u201349, https:\/\/doi.org\/10.1007\/978-3-030-11024-6_3","DOI":"10.1007\/978-3-030-11024-6_3"},{"key":"11060_CR64","unstructured":"Hidalgo G, Raaj Y, Idrees H, Xiang D, Joo H, Simon T, Sheikh Y (2019) Single-network whole-body pose estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6982\u20136991"},{"key":"11060_CR65","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"11060_CR66","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv: org\/1704.04861"},{"key":"11060_CR67","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"11","key":"11060_CR68","doi-asserted-by":"crossref","first-page":"8143","DOI":"10.1007\/s00521-022-08090-8","volume":"35","author":"G Huang","year":"2023","unstructured":"Huang G, Tran SN, Bai Q, Alty J (2023) Real-time automated detection of older adults\u2019 hand gestures in home and clinical settings. Neural Comput Appl 35(11):8143\u20138156","journal-title":"Neural Comput Appl"},{"key":"11060_CR69","doi-asserted-by":"crossref","unstructured":"Huang X, Fu N, Liu S, Ostadabbas S (2021) Invariant representation learning for infant pose estimation with small data. In: 2021 16th IEEE international conference on automatic face and gesture recognition (FG 2021), IEEE, pp 1\u20138","DOI":"10.1109\/FG52635.2021.9666956"},{"key":"11060_CR70","doi-asserted-by":"crossref","unstructured":"Ionescu C, Li F, Sminchisescu C (2011) Latent structured models for human pose estimation. In: Proceedings of the international conference on computer vision, IEEE, pp 2220\u20132227","DOI":"10.1109\/ICCV.2011.6126500"},{"issue":"7","key":"11060_CR71","doi-asserted-by":"crossref","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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11060_CR72","doi-asserted-by":"crossref","unstructured":"Ivanska L, Korotyeyeva T (2022) Mobile real-time gesture detection application for sign language learning. In: 2022 IEEE 17th international conference on computer sciences and information technologies (CSIT), IEEE, pp 511\u2013514","DOI":"10.1109\/CSIT56902.2022.10000440"},{"key":"11060_CR73","doi-asserted-by":"crossref","unstructured":"Jhuang H, Gall J, Zuffi S, Schmid C, Black MJ (2013) Towards understanding action recognition. In: Proceedings of the IEEE international conference on computer vision, pp 3192\u20133199","DOI":"10.1109\/ICCV.2013.396"},{"key":"11060_CR74","doi-asserted-by":"crossref","unstructured":"Jiang H, Grauman K (2017) Seeing invisible poses: estimating 3d body pose from egocentric video. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, IEEE, pp 3501\u20133509","DOI":"10.1109\/CVPR.2017.373"},{"key":"11060_CR75","doi-asserted-by":"crossref","unstructured":"Jin S, Liu W, Xie E, Wang W, Qian C, Ouyang W, Luo P (2020) Differentiable hierarchical graph grouping for multi-person pose estimation. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VII 16, Springer, pp 718\u2013734","DOI":"10.1007\/978-3-030-58571-6_42"},{"key":"11060_CR76","doi-asserted-by":"crossref","unstructured":"Johnson S, Everingham M (2010) Clustered pose and nonlinear appearance models for human pose estimation. In: bmvc, Aberystwyth, UK, p\u00a05","DOI":"10.5244\/C.24.12"},{"key":"11060_CR77","doi-asserted-by":"crossref","unstructured":"Johnson S, Everingham M (2011) Learning effective human pose estimation from inaccurate annotation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, pp 1465\u20131472","DOI":"10.1109\/CVPR.2011.5995318"},{"key":"11060_CR78","doi-asserted-by":"crossref","unstructured":"Joo H, Liu H, Tan L, Gui L, Nabbe B, Matthews I, Kanade T, Nobuhara S, Sheikh Y (2015) Panoptic studio: a massively multiview system for social motion capture. In: Proceedings of the IEEE international conference on computer vision, pp 3334\u20133342","DOI":"10.1109\/ICCV.2015.381"},{"key":"11060_CR79","doi-asserted-by":"crossref","unstructured":"Karjee J, Anand K, Naik P, Dabbiru RBV, Byadgi CS, Srinidhi N (2022) Dynamic split computing of posenet inference for fitness applications in home iot-edge platform. In: 2022 14th international conference on communication systems & networks (COMSNETS), IEEE, pp 430\u2013432","DOI":"10.1109\/COMSNETS53615.2022.9668605"},{"key":"11060_CR80","doi-asserted-by":"crossref","unstructured":"Kato H, Ushiku Y, Harada T (2018) Neural 3d mesh renderer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3907\u20133916","DOI":"10.1109\/CVPR.2018.00411"},{"key":"11060_CR81","doi-asserted-by":"crossref","unstructured":"Kendall A, Grimes M, Cipolla R (2015) Posenet: a convolutional network for real-time 6-dof camera relocalization. In: Proceedings of the IEEE international conference on computer vision, pp 2938\u20132946","DOI":"10.1109\/ICCV.2015.336"},{"key":"11060_CR82","doi-asserted-by":"publisher","DOI":"10.3390\/s21134572","author":"DY Kim","year":"2021","unstructured":"Kim DY, Chang JY (2021) Attention-based 3d human pose sequence refinement network. Sensors. https:\/\/doi.org\/10.3390\/s21134572","journal-title":"Sensors"},{"issue":"3","key":"11060_CR83","doi-asserted-by":"crossref","first-page":"743","DOI":"10.32604\/iasc.2021.019329","volume":"30","author":"MJ Kim","year":"2021","unstructured":"Kim MJ, Hong SP, Kang M, Seo J (2021) Performance comparison of posenet models on an aiot edge device. Intell Automat Soft Comput 30(3):743\u2013753","journal-title":"Intell Automat Soft Comput"},{"key":"11060_CR84","doi-asserted-by":"publisher","DOI":"10.3390\/s22207975","author":"SH Kim","year":"2022","unstructured":"Kim SH, Jeong S, Park S, Chang JY (2022) Camera motion agnostic method for estimating 3d human poses. Sensors. https:\/\/doi.org\/10.3390\/s22207975","journal-title":"Sensors"},{"key":"11060_CR85","unstructured":"Kingma D, Welling M (2014) Auto-encoding variational bayes international. In: Proceedings of the international conference on learning representations"},{"key":"11060_CR86","doi-asserted-by":"crossref","unstructured":"Kocabas M, Karagoz S, Akbas E (2019) Self-supervised learning of 3d human pose using multi-view geometry. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1077\u20131086","DOI":"10.1109\/CVPR.2019.00117"},{"key":"11060_CR87","doi-asserted-by":"crossref","unstructured":"Kocabas M, Athanasiou N, Black MJ (2020) Vibe: Video inference for human body pose and shape estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5253\u20135263","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"11060_CR88","doi-asserted-by":"crossref","unstructured":"Kolotouros N, Pavlakos G, Daniilidis K (2019) Convolutional mesh regression for single-image human shape reconstruction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4501\u20134510","DOI":"10.1109\/CVPR.2019.00463"},{"issue":"2","key":"11060_CR89","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s42979-022-01567-2","volume":"4","author":"S Kulkarni","year":"2023","unstructured":"Kulkarni S, Deshmukh S, Fernandes F, Patil A, Jabade V (2023) Poseanalyser: a survey on human pose estimation. SN Comput Sci 4(2):136","journal-title":"SN Comput Sci"},{"issue":"9","key":"11060_CR90","doi-asserted-by":"publisher","first-page":"13719","DOI":"10.1007\/s11042-022-13728-9","volume":"82","author":"D Kumar","year":"2023","unstructured":"Kumar D, Shafi RM (2023) A fast feature selection technique for real-time face detection using hybrid optimized region based convolutional neural network. Multimed Tools Appl 82(9):13719\u201313732. https:\/\/doi.org\/10.1007\/s11042-022-13728-9","journal-title":"Multimed Tools Appl"},{"key":"11060_CR91","unstructured":"Kumar P, Chauhan S (2023) Towards improvement of baseline performance for regression based human pose estimation. Evol Syst pp 1\u20139"},{"key":"11060_CR92","doi-asserted-by":"crossref","unstructured":"Kundu JN, Seth S, YM P, Jampani V, Chakraborty A, Babu RV (2022) Uncertainty-aware adaptation for self-supervised 3d human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 20448\u201320459","DOI":"10.1109\/CVPR52688.2022.01980"},{"issue":"2","key":"11060_CR93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2735951","volume":"6","author":"M Kyan","year":"2015","unstructured":"Kyan M, Sun G, Li H, Zhong L, Muneesawang P, Dong N, Elder B, Guan L (2015) An approach to ballet dance training through ms kinect and visualization in a cave virtual reality environment. ACM Trans Intell Syst Technol (TIST) 6(2):1\u201337","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"2","key":"11060_CR94","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s10462-017-9578-y","volume":"52","author":"B Lahasan","year":"2019","unstructured":"Lahasan B, Lutfi SL, San-Segundo R (2019) A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression. Artif Intell Rev 52(2):949\u2013979. https:\/\/doi.org\/10.1007\/s10462-017-9578-y","journal-title":"Artif Intell Rev"},{"key":"11060_CR95","doi-asserted-by":"crossref","unstructured":"Li J, Wang C, Zhu H, Mao Y, Fang HS, Lu C (2019a) Crowdpose: Efficient crowded scenes pose estimation and a new benchmark. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10863\u201310872","DOI":"10.1109\/CVPR.2019.01112"},{"issue":"2","key":"11060_CR96","doi-asserted-by":"crossref","first-page":"632","DOI":"10.3390\/s22020632","volume":"22","author":"J Li","year":"2022","unstructured":"Li J, Wang Z, Qi B, Zhang J, Yang H (2022) Meme: a mutually enhanced modeling method for efficient and effective human pose estimation. Sensors 22(2):632","journal-title":"Sensors"},{"key":"11060_CR97","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1109\/tip.2023.3239192","volume":"32","author":"J Li","year":"2023","unstructured":"Li J, Wang Y, Zhang S (2023) Polarpose: single-stage multi-person pose estimation in polar coordinates. IEEE Trans Image Process 32:1108\u20131119. https:\/\/doi.org\/10.1109\/tip.2023.3239192","journal-title":"IEEE Trans Image Process"},{"key":"11060_CR98","doi-asserted-by":"crossref","unstructured":"Li K, Wang S, Zhang X, Xu Y, Xu W, Tu Z (2021a) Pose recognition with cascade transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1944\u20131953","DOI":"10.1109\/CVPR46437.2021.00198"},{"key":"11060_CR99","doi-asserted-by":"crossref","unstructured":"Li M, Chen S, Chen X, Zhang Y, Wang Y, Tian Q (2019b) Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3595\u20133603","DOI":"10.1109\/CVPR.2019.00371"},{"issue":"6","key":"11060_CR100","doi-asserted-by":"publisher","first-page":"3316","DOI":"10.1109\/tpami.2021.3053765","volume":"44","author":"M Li","year":"2022","unstructured":"Li M, Chen S, Chen X, Zhang Y, Wang Y, Tian Q (2022) Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction. IEEE Trans Pattern Anal Mach Intell 44(6):3316\u20133333. https:\/\/doi.org\/10.1109\/tpami.2021.3053765","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11060_CR101","doi-asserted-by":"crossref","unstructured":"Li Q, Zhang Z, Zhang F, Xiao F (2023b) Hrnext: high-resolution context network for crowd pose estimation. IEEE Trans Multimed","DOI":"10.1109\/TMM.2023.3248144"},{"key":"11060_CR102","doi-asserted-by":"crossref","unstructured":"Li S, Ke L, Pratama K, Tai YW, Tang CK, Cheng KT (2020a) Cascaded deep monocular 3d human pose estimation with evolutionary training data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6173\u20136183","DOI":"10.1109\/CVPR42600.2020.00621"},{"issue":"10","key":"11060_CR103","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1049\/ipr2.12850","volume":"17","author":"S Li","year":"2023","unstructured":"Li S, Zhang H, Ma H, Feng J, Jiang M (2023) Csit: channel spatial integrated transformer for human pose estimation. IET Image Proc 17(10):3002\u20133011","journal-title":"IET Image Proc"},{"key":"11060_CR104","doi-asserted-by":"publisher","unstructured":"Li SJ, Chan AB (2015) 3d human pose estimation from monocular images with deep convolutional neural network. In: 12th Asian conference on computer vision (ACCV), pp 332\u2013347,https:\/\/doi.org\/10.1007\/978-3-319-16808-1_23","DOI":"10.1007\/978-3-319-16808-1_23"},{"key":"11060_CR105","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1109\/TMM.2022.3141231","volume":"25","author":"W Li","year":"2022","unstructured":"Li W, Liu H, Ding R, Liu M, Wang P, Yang W (2022) Exploiting temporal contexts with strided transformer for 3d human pose estimation. IEEE Trans Multimed 25:1282\u20131293","journal-title":"IEEE Trans Multimed"},{"key":"11060_CR106","doi-asserted-by":"crossref","unstructured":"Li W, Liu H, Tang H, Wang P, Van Gool L (2022d) Mhformer: Multi-hypothesis transformer for 3d human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13147\u201313156","DOI":"10.1109\/CVPR52688.2022.01280"},{"key":"11060_CR107","doi-asserted-by":"crossref","unstructured":"Li Y, Wang C, Cao Y, Liu B, Tan J, Luo Y (2020b) Human pose estimation based in-home lower body rehabilitation system. In: 2020 International joint conference on neural networks (IJCNN), IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9207296"},{"key":"11060_CR108","doi-asserted-by":"crossref","unstructured":"Li Y, Zhang S, Wang Z, Yang S, Yang W, Xia ST, Zhou E (2021b) Tokenpose: learning keypoint tokens for human pose estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11313\u201311322","DOI":"10.1109\/ICCV48922.2021.01112"},{"key":"11060_CR109","doi-asserted-by":"crossref","unstructured":"Li Y, Mao H, Girshick R, He K (2022e) Exploring plain vision transformer backbones for object detection. In: European conference on computer vision, Springer, pp 280\u2013296","DOI":"10.1007\/978-3-031-20077-9_17"},{"key":"11060_CR110","doi-asserted-by":"crossref","unstructured":"Li Y, Yang S, Liu P, Zhang S, Wang Y, Wang Z, Yang W, Xia ST (2022f) Simcc: a simple coordinate classification perspective for human pose estimation. In: European conference on computer vision, Springer, pp 89\u2013106","DOI":"10.1007\/978-3-031-20068-7_6"},{"key":"11060_CR111","unstructured":"Li Z, Zhou F (2018) Fssd: feature fusion single shot multibox detector. arXiv: org\/1712.00960"},{"key":"11060_CR112","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TIP.2022.3226410","volume":"32","author":"Z Li","year":"2022","unstructured":"Li Z, Gong X, Song R, Duan P, Liu J, Zhang W (2022) Smam: self and mutual adaptive matching for skeleton-based few-shot action recognition. IEEE Trans Image Process 32:392\u2013402","journal-title":"IEEE Trans Image Process"},{"key":"11060_CR113","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2018.10.006","volume":"176","author":"S Liang","year":"2018","unstructured":"Liang S, Sun X, Wei Y (2018) Compositional human pose regression. Comput Vis Image Underst 176:1\u20138. https:\/\/doi.org\/10.1016\/j.cviu.2018.10.006","journal-title":"Comput Vis Image Underst"},{"key":"11060_CR114","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13, Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"11060_CR115","doi-asserted-by":"crossref","unstructured":"Lin W, Liu H, Liu S, Li Y, Xiong H, Qi G, Sebe N (2023) Hieve: a large-scale benchmark for human-centric video analysis in complex events. Int J Comput Vis pp 1\u201325","DOI":"10.1007\/s11263-023-01842-6"},{"issue":"3","key":"11060_CR116","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1007\/s10489-022-03608-y","volume":"53","author":"HB Liu","year":"2023","unstructured":"Liu HB, Fan ZX, Chen Q, Zhang XM (2023) Enhancing face detection in video sequences by video segmentation preprocessing. Appl Intell 53(3):2897\u20132907. https:\/\/doi.org\/10.1007\/s10489-022-03608-y","journal-title":"Appl Intell"},{"issue":"14","key":"11060_CR117","doi-asserted-by":"publisher","first-page":"3433","DOI":"10.1049\/ipr2.12277","volume":"15","author":"L Liu","year":"2021","unstructured":"Liu L, Yang L, Chen WJ, Gao X (2021) Dual-view 3d human pose estimation without camera parameters for action recognition. IET Image Process 15(14):3433\u20133440. https:\/\/doi.org\/10.1049\/ipr2.12277","journal-title":"IET Image Process"},{"key":"11060_CR118","doi-asserted-by":"crossref","unstructured":"Liu Q, Zhang Y, Bai S, Yuille A (2022a) Explicit occlusion reasoning for multi-person 3d human pose estimation. In: European conference on computer vision, Springer, pp 497\u2013517","DOI":"10.1007\/978-3-031-20065-6_29"},{"key":"11060_CR119","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"11060_CR120","doi-asserted-by":"crossref","unstructured":"Liu Y, Yang J, Gu X, Guo Y, Yang GZ (2022b) Ego+ x: An egocentric vision system for global 3d human pose estimation and social interaction characterization. In: 2022 IEEE\/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 5271\u20135277","DOI":"10.1109\/IROS47612.2022.9981710"},{"key":"11060_CR121","doi-asserted-by":"crossref","unstructured":"Liu Y, Yang J, Gu X, Chen Y, Guo Y, Yang GZ (2023b) Egofish3d: egocentric 3d pose estimation from a fisheye camera via self-supervised learning. IEEE Trans Multimed","DOI":"10.36227\/techrxiv.18516119"},{"key":"11060_CR122","doi-asserted-by":"crossref","unstructured":"Liu Z, Chen H, Feng R, Wu S, Ji S, Yang B, Wang X (2021b) Deep dual consecutive network for human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 525\u2013534","DOI":"10.1109\/CVPR46437.2021.00059"},{"key":"11060_CR123","doi-asserted-by":"crossref","unstructured":"Liu Z, Feng R, Chen H, Wu S, Gao Y, Gao Y, Wang X (2022c) Temporal feature alignment and mutual information maximization for video-based human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11006\u201311016","DOI":"10.1109\/CVPR52688.2022.01073"},{"issue":"6","key":"11060_CR124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper M, Mahmood N, Romero J, Pons-Moll G, Black MJ (2015) Smpl: a skinned multi-person linear model. ACM Trans Graph 34(6):1\u201316","journal-title":"ACM Trans Graph"},{"key":"11060_CR125","first-page":"25019","volume":"34","author":"Z Luo","year":"2021","unstructured":"Luo Z, Hachiuma R, Yuan Y, Kitani K (2021) Dynamics-regulated kinematic policy for egocentric pose estimation. Adv Neural Inf Process Syst 34:25019\u201325032","journal-title":"Adv Neural Inf Process Syst"},{"key":"11060_CR126","doi-asserted-by":"crossref","unstructured":"Luo Z, Wang Z, Cai Y, Wang G, Wang L, Huang Y, Zhou E, Tan T, Sun J (2021b) Efficient human pose estimation by learning deeply aggregated representations. In: 2021 IEEE international conference on multimedia and expo (ICME), IEEE, pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428206"},{"key":"11060_CR127","doi-asserted-by":"crossref","unstructured":"Luo Z, Wang Z, Huang Y, Wang L, Tan T, Zhou E (2021c) Rethinking the heatmap regression for bottom-up human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13264\u201313273","DOI":"10.1109\/CVPR46437.2021.01306"},{"key":"11060_CR128","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","DOI":"10.1109\/CVPR.2018.00539"},{"key":"11060_CR129","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.cag.2019.09.002","volume":"85","author":"DC Luvizon","year":"2019","unstructured":"Luvizon DC, Labia H, Picard D (2019) Human pose regression by combining indirect part detection and contextual information. Comput Graph-UK 85:15\u201322. https:\/\/doi.org\/10.1016\/j.cag.2019.09.002","journal-title":"Comput Graph-UK"},{"key":"11060_CR130","doi-asserted-by":"crossref","unstructured":"Ma H, Wang Z, Chen Y, Kong D, Chen L, Liu X, Yan X, Tang H, Xie X (2022) Ppt: token-pruned pose transformer for monocular and multi-view human pose estimation. In: European conference on computer vision, Springer, pp 424\u2013442","DOI":"10.1007\/978-3-031-20065-6_25"},{"key":"11060_CR131","doi-asserted-by":"crossref","unstructured":"Mahmood N, Ghorbani N, Troje NF, Pons-Moll G, Black MJ (2019) Amass: archive of motion capture as surface shapes. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5442\u20135451","DOI":"10.1109\/ICCV.2019.00554"},{"key":"11060_CR132","doi-asserted-by":"crossref","unstructured":"Manesco JRR, Marana AN (2022) A survey of recent advances on two-step 3d human pose estimation. In: Brazilian conference on intelligent systems, Springer, pp 266\u2013281","DOI":"10.1007\/978-3-031-21689-3_20"},{"key":"11060_CR133","doi-asserted-by":"crossref","unstructured":"Mao W, Tian Z, Wang X, Shen C (2021) Fcpose: Fully convolutional multi-person pose estimation with dynamic instance-aware convolutions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9034\u20139043","DOI":"10.1109\/CVPR46437.2021.00892"},{"issue":"8","key":"11060_CR134","doi-asserted-by":"publisher","first-page":"6201","DOI":"10.1007\/s10462-021-09974-2","volume":"54","author":"Y Martinez-Diaz","year":"2021","unstructured":"Martinez-Diaz Y, Nicolas-Diaz M, Mendez-Vazquez H, Luevano LS, Chang L, Gonzalez-Mendoza M, Sucar LE (2021) Benchmarking lightweight face architectures on specific face recognition scenarios. Artif Intell Rev 54(8):6201\u20136244. https:\/\/doi.org\/10.1007\/s10462-021-09974-2","journal-title":"Artif Intell Rev"},{"key":"11060_CR135","doi-asserted-by":"crossref","unstructured":"Mehta D, Rhodin H, Casas D, Fua P, Sotnychenko O, Xu W, Theobalt C (2017a) Monocular 3d human pose estimation in the wild using improved cnn supervision. In: 2017 international conference on 3D vision (3DV), IEEE, pp 506\u2013516","DOI":"10.1109\/3DV.2017.00064"},{"issue":"4","key":"11060_CR136","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073596","volume":"36","author":"D Mehta","year":"2017","unstructured":"Mehta D, Sridhar S, Sotnychenko O, Rhodin H, Shafiei M, Seidel HP, Xu W, Casas D, Theobalt C (2017) Vnect: real-time 3d human pose estimation with a single rgb camera. Acm Trans Graph (tog) 36(4):1\u201314","journal-title":"Acm Trans Graph (tog)"},{"key":"11060_CR137","doi-asserted-by":"crossref","unstructured":"Mehta D, Sotnychenko O, Mueller F, Xu W, Sridhar S, Pons-Moll G, Theobalt C (2018) Single-shot multi-person 3d pose estimation from monocular rgb. In: 2018 International conference on 3D vision (3DV), IEEE, pp 120\u2013130","DOI":"10.1109\/3DV.2018.00024"},{"key":"11060_CR138","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.neucom.2023.03.070","volume":"537","author":"Q Men","year":"2023","unstructured":"Men Q, Ho ES, Shum HP, Leung H (2023) Focalized contrastive view-invariant learning for self-supervised skeleton-based action recognition. Neurocomputing 537:198\u2013209","journal-title":"Neurocomputing"},{"issue":"7","key":"11060_CR139","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.3390\/s17071591","volume":"17","author":"P Merriaux","year":"2017","unstructured":"Merriaux P, Dupuis Y, Boutteau R, Vasseur P, Savatier X (2017) A study of vicon system positioning performance. Sensors 17(7):1591","journal-title":"Sensors"},{"key":"11060_CR140","doi-asserted-by":"crossref","unstructured":"Moon G, Lee KM (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","DOI":"10.1007\/978-3-030-58571-6_44"},{"key":"11060_CR141","doi-asserted-by":"crossref","unstructured":"Moon G, Chang JY, Lee KM (2019) Camera distance-aware top-down approach for 3d multi-person pose estimation from a single rgb image. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10133\u201310142","DOI":"10.1109\/ICCV.2019.01023"},{"key":"11060_CR142","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103628","volume":"128","author":"ZUD Muhammad","year":"2022","unstructured":"Muhammad ZUD, Huang ZJ, Khan R (2022) A review of 3d human body pose estimation and mesh recovery. Digit Signal Prog 128:103628. https:\/\/doi.org\/10.1016\/j.dsp.2022.103628","journal-title":"Digit Signal Prog"},{"key":"11060_CR143","doi-asserted-by":"crossref","first-page":"133330","DOI":"10.1109\/ACCESS.2020.3010248","volume":"8","author":"TL Munea","year":"2020","unstructured":"Munea TL, Jembre YZ, Weldegebriel HT, Chen L, Huang C, Yang C (2020) The progress of human pose estimation: a survey and taxonomy of models applied in 2d human pose estimation. IEEE Access 8:133330\u2013133348","journal-title":"IEEE Access"},{"issue":"5","key":"11060_CR144","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","volume":"31","author":"R Mur-Artal","year":"2015","unstructured":"Mur-Artal R, Montiel JMM, Tardos JD (2015) Orb-slam: a versatile and accurate monocular slam system. IEEE Trans Robot 31(5):1147\u20131163","journal-title":"IEEE Trans Robot"},{"key":"11060_CR145","doi-asserted-by":"crossref","unstructured":"Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VIII 14, Springer, pp 483\u2013499","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"11060_CR146","unstructured":"Nibali A, He Z, Morgan S, Prendergast L (2018) Numerical coordinate regression with convolutional neural networks. arXiv: org\/1801.07372"},{"key":"11060_CR147","doi-asserted-by":"crossref","unstructured":"Nie X, Feng J, Zhang J, Yan S (2019) Single-stage multi-person pose machines. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6951\u20136960","DOI":"10.1109\/ICCV.2019.00705"},{"issue":"5","key":"11060_CR148","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1109\/tmm.2017.2762010","volume":"20","author":"G Ning","year":"2018","unstructured":"Ning G, Zhang Z, He Z (2018) Knowledge-guided deep fractal neural networks for human pose estimation. IEEE Trans Multimed 20(5):1246\u20131259. https:\/\/doi.org\/10.1109\/tmm.2017.2762010","journal-title":"IEEE Trans Multimed"},{"key":"11060_CR149","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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7025\u20137034","DOI":"10.1109\/CVPR.2017.139"},{"key":"11060_CR150","doi-asserted-by":"crossref","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\u20137316","DOI":"10.1109\/CVPR.2018.00763"},{"key":"11060_CR151","doi-asserted-by":"crossref","unstructured":"Pavlakos G, Choutas V, Ghorbani N, Bolkart T, Osman AA, Tzionas D, Black MJ (2019) Expressive body capture: 3d hands, face, and body from a single image. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10975\u201310985","DOI":"10.1109\/CVPR.2019.01123"},{"key":"11060_CR152","doi-asserted-by":"crossref","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","DOI":"10.1109\/CVPR.2019.00794"},{"issue":"5","key":"11060_CR153","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3109\/03091902.2014.909540","volume":"38","author":"A Pfister","year":"2014","unstructured":"Pfister A, West AM, Bronner S, Noah JA (2014) Comparative abilities of microsoft kinect and vicon 3d motion capture for gait analysis. J Med Eng Technol 38(5):274\u2013280","journal-title":"J Med Eng Technol"},{"key":"11060_CR154","doi-asserted-by":"crossref","unstructured":"Pishchulin L, Insafutdinov E, Tang S, Andres B, Andriluka M, Gehler PV, Schiele B (2016) Deepcut: Joint subset partition and labeling for multi person pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4929\u20134937","DOI":"10.1109\/CVPR.2016.533"},{"key":"11060_CR155","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-016-9514-6","volume":"49","author":"C Prakash","year":"2018","unstructured":"Prakash C, Kumar R, Mittal N (2018) Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif Intell Rev 49:1\u201340","journal-title":"Artif Intell Rev"},{"key":"11060_CR156","doi-asserted-by":"crossref","unstructured":"Qiu L, Zhang X, Li Y, Li G, Wu X, Xiong Z, Han X, Cui S (2020) Peeking into occluded joints: a novel framework for crowd pose estimation. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIX 16, Springer, pp 488\u2013504","DOI":"10.1007\/978-3-030-58529-7_29"},{"key":"11060_CR157","doi-asserted-by":"crossref","unstructured":"Qiu Z, Yang Q, Wang J, Fu D (2022) Ivt: An end-to-end instance-guided video transformer for 3d pose estimation. In: Proceedings of the 30th ACM international conference on multimedia, pp 6174\u20136182","DOI":"10.1145\/3503161.3547871"},{"key":"11060_CR158","doi-asserted-by":"crossref","unstructured":"Ramanan D (2006) Learning to parse images of articulated bodies. Adv Neural Inform Process Syst 19","DOI":"10.7551\/mitpress\/7503.003.0146"},{"key":"11060_CR159","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103416","author":"M Rashmi","year":"2022","unstructured":"Rashmi M, Guddeti RMR (2022) Human identification system using 3d skeleton-based gait features and lstm model. J Vis Commun Image Represent. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103416","journal-title":"J Vis Commun Image Represent"},{"key":"11060_CR160","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"11060_CR161","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv: org\/1804.02767"},{"key":"11060_CR162","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"11060_CR163","doi-asserted-by":"crossref","unstructured":"Reimer LM, Weigel S, Ehrenstorfer F, Adikari M, Birkle W, Jonas S (2021) Mobile motion tracking for disease prevention and rehabilitation using apple arkit. In: Navigating healthcare through challenging times. IOS Press, p 78\u201386","DOI":"10.3233\/SHTI210092"},{"key":"11060_CR164","doi-asserted-by":"crossref","first-page":"108046","DOI":"10.1016\/j.patcog.2021.108046","volume":"118","author":"ES dos Reis","year":"2021","unstructured":"dos Reis ES, Seewald LA, Antunes RS et al (2021) Monocular multi-person pose estimation: a survey. Pattern Recognit 118:108046","journal-title":"Pattern Recognit"},{"key":"11060_CR165","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R (eds) Advances in neural information processing systems, vol 28. Curran Associates Inc"},{"key":"11060_CR166","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention, Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"11060_CR167","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"11060_CR168","doi-asserted-by":"crossref","unstructured":"Santesteban I, Garces E, Otaduy MA, Casas D (2020) Softsmpl: data-driven modeling of nonlinear soft-tissue dynamics for parametric humans. In: Comput Graph Forum, Wiley Online Library, pp 65\u201375","DOI":"10.1111\/cgf.13912"},{"key":"11060_CR169","doi-asserted-by":"crossref","unstructured":"Sapp B, Taskar B (2013) Modec: Multimodal decomposable models for human pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3674\u20133681","DOI":"10.1109\/CVPR.2013.471"},{"key":"11060_CR170","unstructured":"Schneider S, Vollmer R (2023) Poses of people in art: a data set for human pose estimation in digital art history. arXiv: org\/2301.05124"},{"issue":"5","key":"11060_CR171","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.1109\/LRA.2023.3259735","volume":"8","author":"J Sedlar","year":"2023","unstructured":"Sedlar J, Stepanova K, Skoviera R, Behrens JK, Tuna M, Sejnova G, Sivic J, Babuska R (2023) Imitrob: imitation learning dataset for training and evaluating 6d object pose estimators. IIEEE Robot Autom Lett 8(5):2788\u20132795","journal-title":"IIEEE Robot Autom Lett"},{"issue":"2","key":"11060_CR172","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s10044-023-01130-6","volume":"26","author":"B Shan","year":"2023","unstructured":"Shan B, Shi Q, Yang F (2023) Msrt: multi-scale representation transformer for regression-based human pose estimation. Pattern Anal Appl 26(2):591\u2013603","journal-title":"Pattern Anal Appl"},{"key":"11060_CR173","doi-asserted-by":"crossref","unstructured":"Shao Z, Liu P, Li Y, Yang J, Zhou X (2021) A multi-level network for human pose estimation. In: 2021 IEEE international conference on robotics and automation (ICRA), IEEE, pp 13085\u201313091","DOI":"10.1109\/ICRA48506.2021.9560980"},{"key":"11060_CR174","doi-asserted-by":"publisher","first-page":"107868","DOI":"10.1016\/j.patcog.2021.107868","volume":"114","author":"W Sheng","year":"2021","unstructured":"Sheng W, Li X (2021) Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network. Pattern Recognit 114:107868. https:\/\/doi.org\/10.1016\/j.patcog.2021.107868","journal-title":"Pattern Recognit"},{"key":"11060_CR175","doi-asserted-by":"crossref","unstructured":"Shi D, Wei X, Yu X, Tan W, Ren Y, Pu S (2021) Inspose: instance-aware networks for single-stage multi-person pose estimation. In: Proceedings of the 29th ACM international conference on multimedia, pp 3079\u20133087","DOI":"10.1145\/3474085.3475447"},{"key":"11060_CR176","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2019) Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12026\u201312035","DOI":"10.1109\/CVPR.2019.01230"},{"issue":"4","key":"11060_CR177","doi-asserted-by":"crossref","first-page":"4122","DOI":"10.1109\/TPAMI.2022.3188716","volume":"45","author":"H Shuai","year":"2022","unstructured":"Shuai H, Wu L, Liu Q (2022) Adaptive multi-view and temporal fusing transformer for 3d human pose estimation. IEEE Trans Pattern Anal Mach Intell 45(4):4122\u20134135","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11060_CR178","unstructured":"SIfre L, Mallat S (2014) Rigid-motion scattering for texture classification. Int J Comput Vis"},{"issue":"1\u20132","key":"11060_CR179","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11263-009-0273-6","volume":"87","author":"L Sigal","year":"2010","unstructured":"Sigal L, Balan AO, Black MJ (2010) Humaneva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int J Comput Vis 87(1\u20132):4\u201327","journal-title":"Int J Comput Vis"},{"key":"11060_CR180","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-023-01003-8","author":"C Song","year":"2023","unstructured":"Song C, Zhu S, Liu Y, Zhang W, Wang Z, Li W, Sun Z, Zhao P, Tian S (2023) Dcnas-net: deformation convolution and neural architecture search detection network for bone marrow oedema. BMC Med Imaging. https:\/\/doi.org\/10.1186\/s12880-023-01003-8","journal-title":"BMC Med Imaging"},{"key":"11060_CR181","doi-asserted-by":"crossref","first-page":"103055","DOI":"10.1016\/j.jvcir.2021.103055","volume":"76","author":"L Song","year":"2021","unstructured":"Song L, Yu G, Yuan J, Liu Z (2021) Human pose estimation and its application to action recognition: a survey. J Vis Commun Image Represent 76:103055","journal-title":"J Vis Commun Image Represent"},{"key":"11060_CR182","doi-asserted-by":"crossref","unstructured":"Song YF, Zhang Z, Shan C, Wang L (2020) Stronger, faster and more explainable: A graph convolutional baseline for skeleton-based action recognition. In: proceedings of the 28th ACM international conference on multimedia, pp 1625\u20131633","DOI":"10.1145\/3394171.3413802"},{"issue":"2","key":"11060_CR183","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1109\/TPAMI.2022.3157033","volume":"45","author":"YF Song","year":"2022","unstructured":"Song YF, Zhang Z, Shan C, Wang L (2022) Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans Pattern Anal Mach Intell 45(2):1474\u20131488","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11060_CR184","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5693\u20135703","DOI":"10.1109\/CVPR.2019.00584"},{"key":"11060_CR185","doi-asserted-by":"crossref","unstructured":"Sun X, Xiao B, Wei F, Liang S, Wei Y (2018) Integral human pose regression. In: Proceedings of the European conference on computer vision (ECCV), pp 529\u2013545","DOI":"10.1007\/978-3-030-01231-1_33"},{"key":"11060_CR186","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"11060_CR187","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"11060_CR188","doi-asserted-by":"crossref","unstructured":"Tao T, Zhang Z, Yang X (2021) Visual perception method based on human pose estimation for humanoid robot imitating human motions. In: Proceedings of the 2021 2nd international conference on control, robotics and intelligent system, pp 54\u201361","DOI":"10.1145\/3483845.3483894"},{"key":"11060_CR189","doi-asserted-by":"crossref","unstructured":"Teepe T, Khan A, Gilg J, Herzog F, H\u00f6rmann S, Rigoll G (2021) Gaitgraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE international conference on image processing (ICIP), IEEE, pp 2314\u20132318","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"11060_CR190","unstructured":"Thakkar K, J Narayanan P (2018) Part-based graph convolutional network for action recognition. arXiv: org\/1809.04983"},{"key":"11060_CR191","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neucom.2021.12.004","volume":"473","author":"H Tian","year":"2022","unstructured":"Tian H, Ma X, Wu H, Li Y (2022) Skeleton-based abnormal gait recognition with spatio-temporal attention enhanced gait-structural graph convolutional networks. Neurocomputing 473:116\u2013126. https:\/\/doi.org\/10.1016\/j.neucom.2021.12.004","journal-title":"Neurocomputing"},{"key":"11060_CR192","unstructured":"Tian Z, Chen H, Shen C (2019) Directpose: Direct end-to-end multi-person pose estimation. arXiv: org\/1911.07451"},{"key":"11060_CR193","doi-asserted-by":"crossref","unstructured":"Tome D, Peluse P, Agapito L, Badino H (2019) xr-egopose: Egocentric 3d human pose from an hmd camera. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7728\u20137738","DOI":"10.1109\/ICCV.2019.00782"},{"key":"11060_CR194","unstructured":"Tompson JJ, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. Adv Neural Inform Process Syst 27"},{"key":"11060_CR195","doi-asserted-by":"publisher","DOI":"10.1145\/3533384","author":"LK Topham","year":"2023","unstructured":"Topham LK, Khan W, Al-Jumeily D, Hussain A (2023) Human body pose estimation for gait identification: a comprehensive survey of datasets and models. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3533384","journal-title":"ACM Comput Surv"},{"key":"11060_CR196","doi-asserted-by":"crossref","unstructured":"Toshev A, Szegedy C (2014) Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1653\u20131660","DOI":"10.1109\/CVPR.2014.214"},{"issue":"6","key":"11060_CR197","doi-asserted-by":"publisher","first-page":"7616","DOI":"10.1007\/s11227-021-04184-7","volume":"78","author":"M Toshpulatov","year":"2022","unstructured":"Toshpulatov M, Lee W, Lee S, Roudsari AH (2022) Human pose, hand and mesh estimation using deep learning: a survey. J Supercomput 78(6):7616\u20137654. https:\/\/doi.org\/10.1007\/s11227-021-04184-7","journal-title":"J Supercomput"},{"key":"11060_CR198","doi-asserted-by":"crossref","unstructured":"Tsochantaridis I, Hofmann T, Joachims T, Altun Y (2004) Support vector machine learning for interdependent and structured output spaces. In: Proceedings of the twenty-first international conference on machine learning, p 104","DOI":"10.1145\/1015330.1015341"},{"key":"11060_CR199","doi-asserted-by":"crossref","first-page":"104657","DOI":"10.1016\/j.imavis.2023.104657","volume":"133","author":"M Umer","year":"2023","unstructured":"Umer M, Sadiq S, Alhebshi RM, Alsubai S, Al Hejaili A, Nappi M, Ashraf I (2023) Face mask detection using deep convolutional neural network and multi-stage image processing. Image Vis Comput 133:104657","journal-title":"Image Vis Comput"},{"key":"11060_CR200","doi-asserted-by":"publisher","unstructured":"Varol G, Ceylan D, Russell B, Yang JM, Yumer E, Laptev I, Schmid C (2018) Bodynet: Volumetric inference of 3d human body shapes. In: 15th European conference on computer Vision (ECCV), pp 20\u201338, https:\/\/doi.org\/10.1007\/978-3-030-01234-2_2","DOI":"10.1007\/978-3-030-01234-2_2"},{"key":"11060_CR201","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inform Process Syst 30"},{"key":"11060_CR202","doi-asserted-by":"crossref","unstructured":"Wandt B, Rosenhahn B (2019) Repnet: weakly supervised training of an adversarial reprojection network for 3d human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7782\u20137791","DOI":"10.1109\/CVPR.2019.00797"},{"key":"11060_CR203","doi-asserted-by":"publisher","first-page":"104260","DOI":"10.1016\/j.engappai.2021.104260","volume":"102","author":"C Wang","year":"2021","unstructured":"Wang C, Zhang F, Ge SS (2021) A comprehensive survey on 2d multi-person pose estimation methods. Eng Appl Artif Intell 102:104260. https:\/\/doi.org\/10.1016\/j.engappai.2021.104260","journal-title":"Eng Appl Artif Intell"},{"key":"11060_CR204","doi-asserted-by":"crossref","first-page":"104219","DOI":"10.1016\/j.dsp.2023.104219","volume":"142","author":"D Wang","year":"2023","unstructured":"Wang D, Xie W, Cai Y, Li X, Liu X (2023) Multi-order spatial interaction network for human pose estimation. Digit Signal Process 142:104219","journal-title":"Digit Signal Process"},{"key":"11060_CR205","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.cag.2023.09.001","volume":"116","author":"D Wang","year":"2023","unstructured":"Wang D, Xie W, Cai Y, Li X, Liu X (2023) Transformer-based rapid human pose estimation network. Comput Graph 116:317\u2013326","journal-title":"Comput Graph"},{"key":"11060_CR206","doi-asserted-by":"crossref","unstructured":"Wang H, Zhou L, Chen Y, Tang M, Wang J (2022a) Regularizing vector embedding in bottom-up human pose estimation. In: European conference on computer vision, Springer, pp 107\u2013122","DOI":"10.1007\/978-3-031-20068-7_7"},{"key":"11060_CR207","doi-asserted-by":"crossref","unstructured":"Wang J, Long X, Gao Y, Ding E, Wen S (2020) Graph-pcnn: Two stage human pose estimation with graph pose refinement. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI 16, Springer, pp 492\u2013508","DOI":"10.1007\/978-3-030-58621-8_29"},{"key":"11060_CR208","doi-asserted-by":"crossref","unstructured":"Wang J, Liu L, Xu W, Sarkar K, Theobalt C (2021b) Estimating egocentric 3d human pose in global space. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11500\u201311509","DOI":"10.1109\/ICCV48922.2021.01130"},{"key":"11060_CR209","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103500","author":"L Wang","year":"2022","unstructured":"Wang L, Chen J, Liu Y (2022) Frame-level refinement networks for skeleton-based gait recognition. Comput Vis Image Underst. https:\/\/doi.org\/10.1016\/j.cviu.2022.103500","journal-title":"Comput Vis Image Underst"},{"issue":"5","key":"11060_CR210","doi-asserted-by":"crossref","first-page":"3941","DOI":"10.1007\/s11063-022-10794-w","volume":"54","author":"R Wang","year":"2022","unstructured":"Wang R, Geng F, Wang X (2022) Mtpose: human pose estimation with high-resolution multi-scale transformers. Neural Process Lett 54(5):3941\u20133964","journal-title":"Neural Process Lett"},{"key":"11060_CR211","doi-asserted-by":"crossref","unstructured":"Wang Y, Li M, Cai H, Chen WM, Han S (2022d) Lite pose: efficient architecture design for 2d human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13126\u201313136","DOI":"10.1109\/CVPR52688.2022.01278"},{"key":"11060_CR212","doi-asserted-by":"crossref","unstructured":"Wei SE, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4724\u20134732","DOI":"10.1109\/CVPR.2016.511"},{"issue":"15","key":"11060_CR213","doi-asserted-by":"crossref","first-page":"3244","DOI":"10.3390\/electronics12153244","volume":"12","author":"C Wu","year":"2023","unstructured":"Wu C, Wei X, Li S, Zhan A (2023) Mstpose: learning-enriched visual information with multi-scale transformers for human pose estimation. Electronics 12(15):3244","journal-title":"Electronics"},{"key":"11060_CR214","doi-asserted-by":"crossref","unstructured":"Wu J, Zheng H, Zhao B, et al (2019) Large-scale datasets for going deeper in image understanding. In: 2019 IEEE International conference on multimedia and expo (ICME), IEEE, pp 1480\u20131485","DOI":"10.1109\/ICME.2019.00256"},{"key":"11060_CR215","doi-asserted-by":"crossref","unstructured":"Wu Q, Xu G, Zhang S, Li Y, Wei F (2020) Human 3d pose estimation in a lying position by rgb-d images for medical diagnosis and rehabilitation. In: 2020 42nd Annual international conference of the IEEE engineering in medicine & biology society (EMBC), IEEE, pp 5802\u20135805","DOI":"10.1109\/EMBC44109.2020.9176407"},{"key":"11060_CR216","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.neucom.2021.11.007","volume":"487","author":"YP Wu","year":"2022","unstructured":"Wu YP, Kong DH, Wang SF, Li JH, Yin BC (2022) Hpgcn: hierarchical poselet-guided graph convolutional network for 3d pose estimation. Neurocomputing 487:243\u2013256. https:\/\/doi.org\/10.1016\/j.neucom.2021.11.007","journal-title":"Neurocomputing"},{"key":"11060_CR217","doi-asserted-by":"crossref","unstructured":"Xia H, Wang Y, Wang X, Xiong S, Yu Z (2022) Hke-gcn: Heatmaps-guided keypoints encoder and graph convolutional network for human pose estimation. In: 2022 International joint conference on neural networks (IJCNN), IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN55064.2022.9892251"},{"key":"11060_CR218","doi-asserted-by":"crossref","unstructured":"Xiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. In: Proceedings of the European conference on computer vision, pp 466\u2013481","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"11060_CR219","unstructured":"Xiao Y, Wang X, Yu D, Su K, Jin L, Song M, Yan S, Zhao J (2022) Adaptivepose++: a powerful single-stage network for multi-person pose regression. arXiv: org\/2210.04014"},{"key":"11060_CR220","unstructured":"Xie S, Zheng W, Xian Z, Yang J, Zhang C, Wu M (2023) Park-detect: towards efficient multi-task satellite imagery road extraction via patch-wise keypoints detection. arXiv: org\/2302.13263"},{"key":"11060_CR221","doi-asserted-by":"crossref","unstructured":"Xu J, Yu Z, Ni B, Yang J, Yang X, Zhang W (2020) Deep kinematics analysis for monocular 3d human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 899\u2013908","DOI":"10.1109\/CVPR42600.2020.00098"},{"key":"11060_CR222","doi-asserted-by":"crossref","unstructured":"Xu L, Guan Y, Jin S, Liu W, Qian C, Luo P, Ouyang W, Wang X (2021) Vipnas: Efficient video pose estimation via neural architecture search. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16072\u201316081","DOI":"10.1109\/CVPR46437.2021.01581"},{"issue":"4","key":"11060_CR223","first-page":"5296","volume":"45","author":"L Xu","year":"2022","unstructured":"Xu L, Jin S, Liu W, Qian C, Ouyang W, Luo P, Wang X (2022) Zoomnas: searching for whole-body human pose estimation in the wild. IEEE Trans Pattern Anal Mach Intell 45(4):5296\u20135313","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"11060_CR224","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/TVCG.2019.2898650","volume":"25","author":"W Xu","year":"2019","unstructured":"Xu W, Chatterjee A, Zollhoefer M, Rhodin H, Fua P, Seidel HP, Theobalt C (2019) Mo 2 cap 2: real-time mobile 3d motion capture with a cap-mounted fisheye camera. IEEE Trans Vis Comput Graph 25(5):2093\u20132101","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"2","key":"11060_CR225","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/thms.2022.3142108","volume":"52","author":"W Xu","year":"2022","unstructured":"Xu W, Xiang D, Wang G, Liao R, Shao M, Li K (2022) Multiview video-based 3-d pose estimation of patients in computer-assisted rehabilitation environment (caren). IEEE T Hum-Mach Syst 52(2):196\u2013206. https:\/\/doi.org\/10.1109\/thms.2022.3142108","journal-title":"IEEE T Hum-Mach Syst"},{"key":"11060_CR226","first-page":"38571","volume":"35","author":"Y Xu","year":"2022","unstructured":"Xu Y, Zhang J, Zhang Q, Tao D (2022) Vitpose: simple vision transformer baselines for human pose estimation. Adv Neural Inf Process Syst 35:38571\u201338584","journal-title":"Adv Neural Inf Process Syst"},{"key":"11060_CR227","unstructured":"Xu Z, Zhang Q (2018) Boundary-aided human body shape and pose estimation from a single image for garment design and manufacture. In: Eurographics (Posters), pp 29\u201330"},{"key":"11060_CR228","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"11060_CR229","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/TIP.2022.3149229","volume":"31","author":"K Yang","year":"2022","unstructured":"Yang K, Gu R, Wang M, Toyoura M, Xu G (2022) Lasor: learning accurate 3d human pose and shape via synthetic occlusion-aware data and neural mesh rendering. IEEE Trans Image Process 31:1938\u20131948","journal-title":"IEEE Trans Image Process"},{"key":"11060_CR230","doi-asserted-by":"crossref","first-page":"108652","DOI":"10.1016\/j.patcog.2022.108652","volume":"128","author":"S Yang","year":"2022","unstructured":"Yang S, Yang W, Cui Z (2022) Searching part-specific neural fabrics for human pose estimation. Pattern Recognit 128:108652","journal-title":"Pattern Recognit"},{"key":"11060_CR231","doi-asserted-by":"crossref","unstructured":"Yang TJ, Howard A, Chen B, Zhang X, Go A, Sandler M, Sze V, Adam H (2018) Netadapt: platform-aware neural network adaptation for mobile applications. In: European conference on computer vision, pp 285\u2013300","DOI":"10.1007\/978-3-030-01249-6_18"},{"key":"11060_CR232","doi-asserted-by":"crossref","unstructured":"Yang Y, Ramanan D (2011) Articulated pose estimation with flexible mixtures-of-parts. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, pp 1385\u20131392","DOI":"10.1109\/CVPR.2011.5995741"},{"key":"11060_CR233","unstructured":"Yao P, Fang Z, Wu F, Feng Y, Li J (2019) Densebody: Directly regressing dense 3d human pose and shape from a single color image. arXiv: org\/1903.10153"},{"key":"11060_CR234","doi-asserted-by":"crossref","unstructured":"Yu C, Xiao B, Gao C, Yuan L, Zhang L, Sang N, Wang J (2021) Lite-hrnet: a lightweight high-resolution network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10440\u201310450","DOI":"10.1109\/CVPR46437.2021.01030"},{"key":"11060_CR235","doi-asserted-by":"crossref","unstructured":"Zhang D, Hao X, Wang D, Qin C, Zhao B, Liang L, Liu W (2023a) An efficient lightweight convolutional neural network for industrial surface defect detection. Artif Intell Rev pp 1\u201327","DOI":"10.1007\/s10462-023-10438-y"},{"key":"11060_CR236","doi-asserted-by":"crossref","unstructured":"Zhang F, Zhu X, Dai H, Ye M, Zhu C (2020a) Distribution-aware coordinate representation for human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7093\u20137102","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"11060_CR237","unstructured":"Zhang H, Ouyang H, Liu S, Qi X, Shen X, Yang R, Jia J (2019a) Human pose estimation with spatial contextual information. arXiv: org\/1901.01760"},{"key":"11060_CR238","doi-asserted-by":"crossref","unstructured":"Zhang H, Hu Z, Sun Z, Zhao M, Bi S, Di J (2023b) A fused convolutional spatio-temporal progressive approach for 3d human pose estimation. Vis Comput pp 1\u201313","DOI":"10.1007\/s00371-023-03088-2"},{"key":"11060_CR239","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhu Z, Lu J, Huang J, Huang G, Zhou J (2021a) Simple: single-network with mimicking and point learning for bottom-up human pose estimation. In: Proceedings of the AAAI conference on artificial intelligence, pp 3342\u20133350","DOI":"10.1609\/aaai.v35i4.16446"},{"key":"11060_CR240","doi-asserted-by":"crossref","unstructured":"Zhang K, Luan X, Syed THS, Xiang X (2023c) Icrformer: an improving cos-reweighting transformer for 3d human pose estimation in video. In: 2023 35th Chinese control and decision conference (CCDC), IEEE, pp 436\u2013441","DOI":"10.1109\/CCDC58219.2023.10326602"},{"issue":"2","key":"11060_CR241","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/tcsvt.2020.2986402","volume":"31","author":"S Zhang","year":"2021","unstructured":"Zhang S, Wen L, Lei Z, Li SZ (2021) Refinedet plus plus: single-shot refinement neural network for object detection. IEEE Trans Circuits Syst Video Technol 31(2):674\u2013687. https:\/\/doi.org\/10.1109\/tcsvt.2020.2986402","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11060_CR242","doi-asserted-by":"crossref","unstructured":"Zhang SH, Li R, Dong X, Rosin P, Cai Z, Han X, Yang D, Huang H, Hu SM (2019b) Pose2seg: detection free human instance segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 889\u2013898","DOI":"10.1109\/CVPR.2019.00098"},{"key":"11060_CR243","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.imavis.2017.02.002","volume":"61","author":"W Zhang","year":"2017","unstructured":"Zhang W, Liu Z, Zhou L, Leung H, Chan AB (2017) Martial arts, dancing and sports dataset: a challenging stereo and multi-view dataset for 3d human pose estimation. Image Vis Comput 61:22\u201339. https:\/\/doi.org\/10.1016\/j.imavis.2017.02.002","journal-title":"Image Vis Comput"},{"issue":"8","key":"11060_CR244","doi-asserted-by":"publisher","first-page":"3047","DOI":"10.1109\/tnnls.2019.2935173","volume":"31","author":"X Zhang","year":"2020","unstructured":"Zhang X, Xu C, Tian X, Tao D (2020) Graph edge convolutional neural networks for skeleton-based action recognition. IEEE Trans Neural Netw Learn Syst 31(8):3047\u20133060. https:\/\/doi.org\/10.1109\/tnnls.2019.2935173","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11060_CR245","doi-asserted-by":"crossref","first-page":"103505","DOI":"10.1016\/j.cviu.2022.103505","volume":"222","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, You S, Karaoglu S, Gevers T (2022) Multi-person 3d pose estimation from a single image captured by a fisheye camera. Comput Vis Image Underst 222:103505","journal-title":"Comput Vis Image Underst"},{"key":"11060_CR246","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s11263-020-01398-9","volume":"129","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Wang C, Qiu W, Qin W, Zeng W (2021) Adafuse: adaptive multiview fusion for accurate human pose estimation in the wild. Int J Comput Vis 129:703\u2013718","journal-title":"Int J Comput Vis"},{"key":"11060_CR247","doi-asserted-by":"crossref","unstructured":"Zhao L, Peng X, Tian Y, Kapadia M, Metaxas DN (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","DOI":"10.1109\/CVPR.2019.00354"},{"key":"11060_CR248","doi-asserted-by":"crossref","unstructured":"Zhao X, Guo C, Zou Q (2021) Human pose estimation with gated multi-scale feature fusion and spatial mutual information. Visual Comput pp 1\u201319","DOI":"10.1007\/s00371-021-02317-w"},{"key":"11060_CR249","doi-asserted-by":"crossref","unstructured":"Zheng C, Zhu S, Mendieta M, Yang T, Chen C, Ding Z (2021) 3d human pose estimation with spatial and temporal transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11656\u201311665","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"11060_CR250","doi-asserted-by":"crossref","unstructured":"Zhou H, Gao Y, Liu W, Jiang Y, Dong W (2020) Posture tracking meets fitness coaching: A two-phase optimization approach with wearable devices. In: 2020 IEEE 17th international conference on mobile ad hoc and sensor systems (MASS), IEEE, pp 524\u2013532","DOI":"10.1109\/MASS50613.2020.00070"},{"issue":"1","key":"11060_CR252","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3390\/e25010020","volume":"25","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Xu C, Zhao L, Zhu A, Hu F, Li Y (2022) Csi-former: pay more attention to pose estimation with wifi. Entropy 25(1):20","journal-title":"Entropy"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-11060-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-11060-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-11060-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T11:34:26Z","timestamp":1738582466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-11060-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":251,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["11060"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-11060-2","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"30 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","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 have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"68"}}