{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T09:46:17Z","timestamp":1785059177064,"version":"3.55.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872112"],"award-info":[{"award-number":["61872112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072141"],"award-info":[{"award-number":["62072141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11263-023-01770-5","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T12:06:13Z","timestamp":1678277173000},"page":"1566-1583","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Learning Enriched Hop-Aware Correlation for Robust 3D Human Pose Estimation"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5200-3420","authenticated-orcid":false,"given":"Shengping","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiang","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxun","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingming","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"1770_CR1","unstructured":"Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Steeg, G. V., & Galstyan, A. (2019). Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML."},{"issue":"1","key":"1770_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/TPAMI.2006.21","volume":"28","author":"A Agarwal","year":"2006","unstructured":"Agarwal, A., & Triggs, B. (2006). Recovering 3D human pose from monocular images. TPAMI, 28(1), 44\u201358.","journal-title":"TPAMI"},{"key":"1770_CR3","doi-asserted-by":"crossref","unstructured":"Bogo, F., Kanazawa, A., Lassner, C., Gehler, P. V., Romero, J., & Black, M. J. (2016). Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In ECCV.","DOI":"10.1007\/978-3-319-46454-1_34"},{"key":"1770_CR4","doi-asserted-by":"crossref","unstructured":"Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T., Yuan, J., & Magnenat-Thalmann, N. (2019). Exploiting spatial-temporal relationships for 3d pose estimation via graph convolutional networks. In ICCV.","DOI":"10.1109\/ICCV.2019.00236"},{"key":"1770_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C., & Ramanan, D. (2017). 3D human pose estimation = 2D pose estimation + matching. In CVPR.","DOI":"10.1109\/CVPR.2017.610"},{"key":"1770_CR6","doi-asserted-by":"crossref","unstructured":"Chen, C., Tyagi, A., Agrawal, A., Drover, D., MV, R., Stojanov, S., & Rehg, J. M. (2019a). Unsupervised 3d pose estimation with geometric self-supervision. In CVPR.","DOI":"10.1109\/CVPR.2019.00586"},{"issue":"1","key":"1770_CR7","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/TCSVT.2021.3057267","volume":"32","author":"T Chen","year":"2021","unstructured":"Chen, T., Fang, C., Shen, X., Zhu, Y., Chen, Z., & Luo, J. (2021). Anatomy-aware 3D human pose estimation in videos. IEEE Transactions on Circuits and Systems for Video Technology, 32(1), 198\u2013209.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"1770_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Huang, S., Yuan, T., Zhu, Y., Qi, S., & Zhu, S. (2019b) Holistic++ scene understanding: Single-view 3D holistic scene parsing and human pose estimation with human-object interaction and physical commonsense. In ICCV.","DOI":"10.1109\/ICCV.2019.00874"},{"key":"1770_CR9","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 CVPR.","DOI":"10.1109\/CVPR.2018.00742"},{"key":"1770_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Z., Huang, Y., Yu, H., Xue, B., Han, K., Guo, Y., & Wang, L. (2020). Towards part-aware monocular 3D human pose estimation: An architecture search approach. In ECCV.","DOI":"10.1007\/978-3-030-58580-8_42"},{"key":"1770_CR11","doi-asserted-by":"crossref","unstructured":"Ci, H., Wang, C., Ma, X., & Wang, Y. (2019). Optimizing network structure for 3D human pose estimation. In ICCV.","DOI":"10.1109\/ICCV.2019.00235"},{"key":"1770_CR12","unstructured":"Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS."},{"key":"1770_CR13","doi-asserted-by":"crossref","unstructured":"Doosti, B., Naha, S., Mirbagheri, M., & Crandall, D. J. (2020) Hope-net: A graph-based model for hand-object pose estimation. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00664"},{"key":"1770_CR14","unstructured":"Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., G\u00f3mez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. In NIPS."},{"key":"1770_CR15","doi-asserted-by":"crossref","unstructured":"Fang, H., Xu, Y., Wang, W., Liu, X., & Zhu, S. (2018). Learning pose grammar to encode human body configuration for 3D pose estimation. In AAAI.","DOI":"10.1609\/aaai.v32i1.12270"},{"key":"1770_CR16","doi-asserted-by":"crossref","unstructured":"Fang, Q., Shuai, Q., Dong, J., Bao, H., & Zhou, X. (2021). Reconstructing 3d human pose by watching humans in the mirror. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01262"},{"key":"1770_CR17","doi-asserted-by":"crossref","unstructured":"Garcia-Hernando, G., Yuan, S., Baek, S., & Kim, T. (2018). First-person hand action benchmark with RGB-D videos and 3D hand pose annotations. In CVPR.","DOI":"10.1109\/CVPR.2018.00050"},{"key":"1770_CR18","unstructured":"Hamilton, W. L, Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. In NIPS."},{"key":"1770_CR19","doi-asserted-by":"crossref","unstructured":"Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M. J., Laptev, I., & Schmid, C. (2019). Learning joint reconstruction of hands and manipulated objects. In CVPR.","DOI":"10.1109\/CVPR.2019.01208"},{"key":"1770_CR20","unstructured":"Henaff, M., Bruna, J., & LeCun, Y. (2015). Deep convolutional networks on graph-structured data. arXiv:1506.05163"},{"key":"1770_CR21","doi-asserted-by":"crossref","unstructured":"Hossain, M. R. I., & Little, J. J. (2018). Exploiting temporal information for 3D human pose estimation. In ECCV.","DOI":"10.1007\/978-3-030-01249-6_5"},{"key":"1770_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In CVPR.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1770_CR23","doi-asserted-by":"crossref","unstructured":"Hu, W., Zhang, C., Zhan, F., Zhang, L., & Wong, T. (2021). Conditional directed graph convolution for 3d human pose estimation. In ACM MM.","DOI":"10.1145\/3474085.3475219"},{"issue":"7","key":"1770_CR24","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2014","unstructured":"Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014). Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments. TPAMI, 36(7), 1325\u20131339.","journal-title":"TPAMI"},{"key":"1770_CR25","unstructured":"Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In ICLR."},{"key":"1770_CR26","unstructured":"Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"1770_CR27","doi-asserted-by":"crossref","unstructured":"Lee, K., Lee, I., & Lee, S. (2018). Propagating LSTM: 3D pose estimation based on joint interdependency. In ECCV.","DOI":"10.1007\/978-3-030-01234-2_8"},{"key":"1770_CR28","doi-asserted-by":"crossref","unstructured":"Li, G., M\u00fcller, M., Thabet, A. K., & Ghanem, B. (2019). Deepgcns: Can GCNs go as deep as CNNs? In ICCV.","DOI":"10.1109\/ICCV.2019.00936"},{"key":"1770_CR29","unstructured":"Li, H., Shi, B., Dai, W., Chen, Y., Wang, B., Sun, Y., Guo, M., Li, C., Zou, J., & Xiong, H. (2021). Hierarchical graph networks for 3D human pose estimation. In BMVC."},{"key":"1770_CR30","unstructured":"Li, S., & Chan, A. B. (2014). 3D human pose estimation from monocular images with deep convolutional neural network. In ACCV."},{"key":"1770_CR31","doi-asserted-by":"crossref","unstructured":"Li, S., Zhang, W., Chan, A. B. (2017). Maximum-margin structured learning with deep networks for 3D human pose estimation. IJCV.","DOI":"10.1007\/s11263-016-0962-x"},{"key":"1770_CR32","doi-asserted-by":"crossref","unstructured":"Li, S., Ke, L., Pratama, K., Tai, Y., Tang, C., & Cheng, K. (2020). Cascaded deep monocular 3D human pose estimation with evolutionary training data. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00621"},{"key":"1770_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R. B., He, K., Hariharan, B., & Belongie, S. J. (2017). Feature pyramid networks for object detection. In CVPR.","DOI":"10.1109\/CVPR.2017.106"},{"key":"1770_CR34","doi-asserted-by":"crossref","unstructured":"Liu, K., Ding, R., Zou, Z., Wang, L., & Tang, W. (2020a). A comprehensive study of weight sharing in graph networks for 3D human pose estimation. In ECCV.","DOI":"10.1007\/978-3-030-58607-2_19"},{"key":"1770_CR35","doi-asserted-by":"crossref","unstructured":"Liu, K., Zou, Z., & Tang, W. (2020b). Learning global pose features in graph convolutional networks for 3D human pose estimation. In ACCV.","DOI":"10.1007\/978-3-030-69525-5_6"},{"key":"1770_CR36","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","volume":"68","author":"M Liu","year":"2017","unstructured":"Liu, M., Liu, H., & Chen, C. (2017). Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognition, 68, 346\u2013362.","journal-title":"Pattern Recognition"},{"key":"1770_CR37","doi-asserted-by":"crossref","unstructured":"Liu, M., & Yuan, J. (2018). Recognizing human actions as the evolution of pose estimation maps. In CVPR.","DOI":"10.1109\/CVPR.2018.00127"},{"key":"1770_CR38","doi-asserted-by":"crossref","unstructured":"Liu, R., Shen, J., Wang, H., Chen, C., Cheung, S. S., & Asari, V. K. (2020c) Attention mechanism exploits temporal contexts: Real-time 3D human pose reconstruction. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00511"},{"key":"1770_CR39","doi-asserted-by":"crossref","unstructured":"Liu, R., Shen, J., Wang, H., Chen, C., Cheung, S. S., & Asari, V. K. (2021) Enhanced 3D human pose estimation from videos by using attention-based neural network with dilated convolutions. IJCV.","DOI":"10.1007\/s11263-021-01436-0"},{"key":"1770_CR40","unstructured":"Luo, C., Chu, X., & Yuille, A. L. (2018). Orinet: A fully convolutional network for 3D human pose estimation. In BMVC."},{"key":"1770_CR41","doi-asserted-by":"crossref","unstructured":"Luvizon, D. C., Picard, D., & Tabia, H. (2022). Consensus-based optimization for 3D human pose estimation in camera coordinates. IJCV.","DOI":"10.1007\/s11263-021-01570-9"},{"key":"1770_CR42","doi-asserted-by":"crossref","unstructured":"Martinez, J., Hossain, R., Romero, J., & Little, J. J. (2017). A simple yet effective baseline for 3D human pose estimation. In ICCV.","DOI":"10.1109\/ICCV.2017.288"},{"key":"1770_CR43","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 3DV.","DOI":"10.1109\/3DV.2017.00064"},{"key":"1770_CR44","doi-asserted-by":"crossref","unstructured":"Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H., Xu, W., Casas, D., & Theobalt, C. (2017b). Vnect: Real-time 3D human pose estimation with a single RGB camera. ACM Transactions on Graphics, 36(4), 44:1\u201344:14.","DOI":"10.1145\/3072959.3073596"},{"key":"1770_CR45","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 ECCV.","DOI":"10.1007\/978-3-030-58571-6_44"},{"key":"1770_CR46","doi-asserted-by":"crossref","unstructured":"Mueller, F., Bernard, F., Sotnychenko, O., Mehta, D., Sridhar, S., Casas, D., & Theobalt, C. (2018). Ganerated hands for real-time 3D hand tracking from monocular RGB. In CVPR.","DOI":"10.1109\/CVPR.2018.00013"},{"key":"1770_CR47","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., & Deng, J. (2016). Stacked hourglass networks for human pose estimation. In ECCV.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"1770_CR48","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Derpanis, K. G., & Daniilidis, K. (2017). Coarse-to-fine volumetric prediction for single-image 3D human pose. In CVPR.","DOI":"10.1109\/CVPR.2017.139"},{"key":"1770_CR49","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 CVPR.","DOI":"10.1109\/CVPR.2019.00794"},{"issue":"3","key":"1770_CR50","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s13218-021-00727-5","volume":"35","author":"J Pustejovsky","year":"2021","unstructured":"Pustejovsky, J., & Krishnaswamy, N. (2021). Embodied human computer interaction. K\u00fcnstliche Intell, 35(3), 307\u2013327.","journal-title":"K\u00fcnstliche Intell"},{"key":"1770_CR51","unstructured":"Quan, J., & Hamza, A. B. (2021). Higher-order implicit fairing networks for 3D human pose estimation. In BMVC."},{"key":"1770_CR52","doi-asserted-by":"crossref","unstructured":"Sharma, S., Varigonda, P. T., Bindal, P., Sharma, A., & Jain, A. (2019). Monocular 3d human pose estimation by generation and ordinal ranking. In ICCV.","DOI":"10.1109\/ICCV.2019.00241"},{"issue":"1","key":"1770_CR53","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929\u20131958.","journal-title":"The Journal of Machine Learning Research"},{"key":"1770_CR54","doi-asserted-by":"crossref","unstructured":"Sun, X., Shang, J., Liang, S., & Wei, Y. (2017). Compositional human pose regression. In ICCV.","DOI":"10.1109\/ICCV.2017.284"},{"key":"1770_CR55","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.robot.2015.03.001","volume":"70","author":"W Takano","year":"2015","unstructured":"Takano, W., & Nakamura, Y. (2015). Action database for categorizing and inferring human poses from video sequences. Robotics and Autonomous Systems, 70, 116\u2013125.","journal-title":"Robotics and Autonomous Systems"},{"key":"1770_CR56","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In NIPS."},{"key":"1770_CR57","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., & Bengio, Y. (2018). Graph attention networks. In ICLR."},{"key":"1770_CR58","doi-asserted-by":"crossref","unstructured":"Wandt B, Ackermann, H., & Rosenhahn, B. (2018). A kinematic chain space for monocular motion capture. In ECCV.","DOI":"10.1007\/978-3-030-11018-5_4"},{"key":"1770_CR59","doi-asserted-by":"crossref","unstructured":"Wang, G., Ying, R., Huang, J., & Leskovec, J. (2021a). Multi-hop attention graph neural networks. In IJCAI.","DOI":"10.24963\/ijcai.2021\/425"},{"issue":"10","key":"1770_CR60","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2021","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., & Xiao, B. (2021). Deep high-resolution representation learning for visual recognition. TPAMI, 43(10), 3349\u20133364.","journal-title":"TPAMI"},{"key":"1770_CR61","doi-asserted-by":"crossref","unstructured":"Wang, J., Yan, S., Xiong, Y., & Lin, D. (2020). Motion guided 3D pose estimation from videos. In ECCV.","DOI":"10.1007\/978-3-030-58601-0_45"},{"key":"1770_CR62","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, Y., Guo, Z., Qian, K., Lin, M., Li, H., & Ren, J. S. J. (2019). Generalizing monocular 3D human pose estimation in the wild. In ICCV.","DOI":"10.1109\/ICCVW.2019.00497"},{"key":"1770_CR63","doi-asserted-by":"crossref","unstructured":"Xie, K., Wang, T., Iqbal, U., Guo, Y., Fidler, S., & Shkurti, F. (2021). Physics-based human motion estimation and synthesis from videos. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01133"},{"key":"1770_CR64","unstructured":"Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., & Liu, T. (2020). On layer normalization in the transformer architecture. In ICML."},{"key":"1770_CR65","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., & Jegelka, S. (2018). Representation learning on graphs with jumping knowledge networks. In ICML."},{"key":"1770_CR66","doi-asserted-by":"crossref","unstructured":"Xu, T., & Takano, W. (2021). Graph stacked hourglass networks for 3D human pose estimation. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01584"},{"key":"1770_CR67","doi-asserted-by":"crossref","unstructured":"Yang, W., Ouyang, W., Wang, X., Ren, J. S. J., Li, H., & Wang, X. (2018). 3D human pose estimation in the wild by adversarial learning. In CVPR.","DOI":"10.1109\/CVPR.2018.00551"},{"key":"1770_CR68","doi-asserted-by":"crossref","unstructured":"Zhao, L., Peng, X., Tian, Y., Kapadia, M., & Metaxas, D. N. (2019). Semantic graph convolutional networks for 3D human pose regression. In CVPR.","DOI":"10.1109\/CVPR.2019.00354"},{"key":"1770_CR69","doi-asserted-by":"crossref","unstructured":"Zhao, W., Tian, Y., Ye, Q., Jiao, J., & Wang, W. (2022). Graformer: Graph convolution transformer for 3D pose estimation","DOI":"10.1109\/CVPR52688.2022.01979"},{"key":"1770_CR70","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 ICCV.","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"1770_CR71","doi-asserted-by":"crossref","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 ICCV.","DOI":"10.1109\/ICCV.2019.00243"},{"key":"1770_CR72","doi-asserted-by":"crossref","unstructured":"Zhou, X., Huang, Q., Sun, X., Xue, X., & Wei, Y. (2017). Towards 3D human pose estimation in the wild: A weakly-supervised approach. In ICCV.","DOI":"10.1109\/ICCV.2017.51"},{"key":"1770_CR73","doi-asserted-by":"crossref","unstructured":"Zou, Z., & Tang, W. (2021). Modulated graph convolutional network for 3D human pose estimation. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01128"},{"key":"1770_CR74","doi-asserted-by":"crossref","unstructured":"Zou, Z., Liu, K., Wang, L., & Tang, W. (2020). High-order graph convolutional networks for 3D human pose estimation. In BMVC.","DOI":"10.1109\/ICCV48922.2021.01128"}],"updated-by":[{"DOI":"10.1007\/s11263-023-01786-x","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000}}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01770-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01770-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01770-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:10:17Z","timestamp":1729037417000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01770-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,8]]},"references-count":74,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1770"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01770-5","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s11263-023-01786-x","asserted-by":"object"}]},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,8]]},"assertion":[{"value":"31 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11263-023-01786-x","URL":"https:\/\/doi.org\/10.1007\/s11263-023-01786-x","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}