{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:33:25Z","timestamp":1772181205415,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00530-024-01441-6","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T05:02:16Z","timestamp":1723611736000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["3D human pose estimation method based on multi-constrained dilated convolutions"],"prefix":"10.1007","volume":"30","author":[{"given":"Huaijun","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingqian","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhuai","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"1441_CR1","doi-asserted-by":"crossref","unstructured":"Shen, J., Sun, Y.: Privacy-preserved video monitoring method with 3d human pose estimation. In: 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 1502\u20131507. IEEE (2023)","DOI":"10.1109\/CSCWD57460.2023.10152735"},{"issue":"1","key":"1441_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3603618","volume":"56","author":"C Zheng","year":"2023","unstructured":"Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., Kehtarnavaz, N., Shah, M.: Deep learning-based human pose estimation: a survey. ACM Comput. Surv. 56(1), 1\u201337 (2023)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"1441_CR3","first-page":"2132138","volume":"2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., An, Y., Wang, K., Li, S.: Wearable sensor-based human activity recognition using hybrid deep learning techniques. Secur. Commun. Netw. 2020(1), 2132138 (2020)","journal-title":"Secur. Commun. Netw."},{"issue":"10","key":"1441_CR4","doi-asserted-by":"publisher","first-page":"3476","DOI":"10.1109\/TPAMI.2020.2985708","volume":"43","author":"J Gao","year":"2020","unstructured":"Gao, J., Zhang, T., Xu, C.: Learning to model relationships for zero-shot video classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3476\u20133491 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"1441_CR5","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/TCSVT.2021.3075470","volume":"32","author":"J Gao","year":"2021","unstructured":"Gao, J., Xu, C.: Learning video moment retrieval without a single annotated video. IEEE Trans. Circuits Syst. Video Technol. 32(3), 1646\u20131657 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1441_CR6","doi-asserted-by":"publisher","first-page":"5410","DOI":"10.1109\/TMM.2023.3333206","volume":"26","author":"Y Hu","year":"2023","unstructured":"Hu, Y., Gao, J., Dong, J., Fan, B., Liu, H.: Exploring rich semantics for open-set action recognition. IEEE Trans. Multimed. 26, 5410\u20135421 (2023)","journal-title":"IEEE Trans. Multimed."},{"key":"1441_CR7","doi-asserted-by":"crossref","unstructured":"Yamakawa, A., Ishikawa, T., Watanabe, H.: Study on improvement of estimation accuracy in pose estimation model using time series correlation. In: 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), pp. 409\u2013412. IEEE (2020)","DOI":"10.1109\/GCCE50665.2020.9291962"},{"issue":"3","key":"1441_CR8","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1109\/TCSVT.2022.3209160","volume":"33","author":"C Papaioannidis","year":"2022","unstructured":"Papaioannidis, C., Mademlis, I., Pitas, I.: Fast cnn-based single-person 2d human pose estimation for autonomous systems. IEEE Trans. Circuits Syst. Video Technol. 33(3), 1262\u20131275 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1441_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108487","volume":"124","author":"V Mazzia","year":"2022","unstructured":"Mazzia, V., Angarano, S., Salvetti, F., Angelini, F., Chiaberge, M.: Action transformer: a self-attention model for short-time pose-based human action recognition. Pattern Recogn. 124, 108487 (2022)","journal-title":"Pattern Recogn."},{"key":"1441_CR10","doi-asserted-by":"publisher","first-page":"15949","DOI":"10.1109\/TPAMI.2023.3311447","volume":"45","author":"J Gao","year":"2023","unstructured":"Gao, J., Chen, M., Xu, C.: Vectorized evidential learning for weakly-supervised temporal action localization. IEEE Trans. Pattern Anal. Mach. Intell. 45, 15949\u201315963 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1441_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2022.3185146","author":"G Wang","year":"2022","unstructured":"Wang, G., Zeng, H., Wang, Z., Liu, Z., Wang, H.: Motion projection consistency based 3d human pose estimation with virtual bones from monocular videos. IEEE Trans. Cogn. Dev. Syst. (2022). https:\/\/doi.org\/10.1109\/TCDS.2022.3185146","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"1441_CR12","doi-asserted-by":"crossref","unstructured":"Wang, J., Qiu, K., Peng, H., Fu, J., Zhu, J.: Ai coach: deep human pose estimation and analysis for personalized athletic training assistance. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 374\u2013382 (2019)","DOI":"10.1145\/3343031.3350910"},{"key":"1441_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108934","volume":"132","author":"C Han","year":"2022","unstructured":"Han, C., Yu, X., Gao, C., Sang, N., Yang, Y.: Single image based 3d human pose estimation via uncertainty learning. Pattern Recogn. 132, 108934 (2022)","journal-title":"Pattern Recogn."},{"key":"1441_CR14","doi-asserted-by":"crossref","unstructured":"Ci, H., Wu, M., Zhu, W., Ma, X., Dong, H., Zhong, F., Wang, Y.: Gfpose: learning 3d human pose prior with gradient fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4800\u20134810 (2023)","DOI":"10.1109\/CVPR52729.2023.00465"},{"issue":"6","key":"1441_CR15","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.vrih.2020.04.005","volume":"2","author":"X Ji","year":"2020","unstructured":"Ji, X., Fang, Q., Dong, J., Shuai, Q., Jiang, W., Zhou, X.: A survey on monocular 3d human pose estimation. Virt. Reality Intell. Hardware 2(6), 471\u2013500 (2020)","journal-title":"Virt. Reality Intell. Hardware"},{"key":"1441_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107236","volume":"104","author":"W Xu","year":"2021","unstructured":"Xu, W., Wu, M., Zhu, J., Zhao, M.: Multi-scale skeleton adaptive weighted gcn for skeleton-based human action recognition in iot. Appl. Soft Comput. 104, 107236 (2021)","journal-title":"Appl. Soft Comput."},{"key":"1441_CR17","doi-asserted-by":"crossref","unstructured":"Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., Fua, P.: Structured prediction of 3d human pose with deep neural networks. arXiv preprint arXiv:1605.05180 (2016)","DOI":"10.5244\/C.30.130"},{"key":"1441_CR18","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: 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 (2017)","DOI":"10.1109\/CVPR.2017.139"},{"issue":"1","key":"1441_CR19","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1007\/s10489-022-03516-1","volume":"53","author":"B-K Gao","year":"2023","unstructured":"Gao, B.-K., Zhang, Z.-X., Wu, C.-N., Wu, C.-L., Bi, H.-B.: Staged cascaded network for monocular 3d human pose estimation. Appl. Intell. 53(1), 1021\u20131029 (2023)","journal-title":"Appl. Intell."},{"key":"1441_CR20","doi-asserted-by":"crossref","unstructured":"Moon, G., Lee, K.M.: 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, pp. 752\u2013768. Springer (2020)","DOI":"10.1007\/978-3-030-58571-6_44"},{"key":"1441_CR21","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Daniilidis, K.: Ordinal depth supervision for 3d human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7307\u20137316 (2018)","DOI":"10.1109\/CVPR.2018.00763"},{"key":"1441_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109497","volume":"139","author":"Z Qiu","year":"2023","unstructured":"Qiu, Z., Qiu, K., Fu, J., Fu, D.: Weakly-supervised pre-training for 3d human pose estimation via perspective knowledge. Pattern Recogn. 139, 109497 (2023)","journal-title":"Pattern Recogn."},{"issue":"6","key":"1441_CR23","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s00138-022-01334-6","volume":"33","author":"H Yang","year":"2022","unstructured":"Yang, H., Guo, L., Zhang, Y., Wu, X.: U-shaped spatial-temporal transformer network for 3d human pose estimation. Mach. Vis. Appl. 33(6), 82 (2022)","journal-title":"Mach. Vis. Appl."},{"key":"1441_CR24","doi-asserted-by":"crossref","unstructured":"Martinez, J., Hossain, R., Romero, J., Little, J.J.: A simple yet effective baseline for 3d human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2640\u20132649 (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"1441_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110267","volume":"140","author":"H Yang","year":"2023","unstructured":"Yang, H., Liu, H., Zhang, Y., Wu, X.: Hierarchical parallel multi-scale graph network for 3d human pose estimation. Appl. Soft Comput. 140, 110267 (2023)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"1441_CR26","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.: Anatomy-aware 3d human pose estimation with bone-based pose decomposition. IEEE Trans. Circuits Syst. Video Technol. 32(1), 198\u2013209 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1441_CR27","doi-asserted-by":"publisher","first-page":"133330","DOI":"10.1109\/ACCESS.2020.3010248","volume":"8","author":"TL Munea","year":"2020","unstructured":"Munea, T.L., Jembre, Y.Z., Weldegebriel, H.T., Chen, L., Huang, C., Yang, C.: The progress of human pose estimation: a survey and taxonomy of models applied in 2d human pose estimation. IEEE Access 8, 133330\u2013133348 (2020)","journal-title":"IEEE Access"},{"key":"1441_CR28","doi-asserted-by":"crossref","unstructured":"Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T.-J., Yuan, J., Thalmann, N.M.: 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 (2019)","DOI":"10.1109\/ICCV.2019.00236"},{"key":"1441_CR29","doi-asserted-by":"crossref","unstructured":"Wang, J., Yan, S., Xiong, Y., Lin, D.: Motion guided 3d pose estimation from videos. In: European Conference on Computer Vision, pp. 764\u2013780. Springer (2020)","DOI":"10.1007\/978-3-030-58601-0_45"},{"key":"1441_CR30","doi-asserted-by":"crossref","unstructured":"Toshev, A., Szegedy, C.: Deeppose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653\u20131660 (2014)","DOI":"10.1109\/CVPR.2014.214"},{"key":"1441_CR31","doi-asserted-by":"crossref","unstructured":"Sun, X., Shang, J., Liang, S., Wei, Y.: Compositional human pose regression. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2602\u20132611 (2017)","DOI":"10.1109\/ICCV.2017.284"},{"key":"1441_CR32","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, C., Zhu, H., Mao, Y., Fang, H.-S., Lu, C.: 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 (2019)","DOI":"10.1109\/CVPR.2019.01112"},{"key":"1441_CR33","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., Deng, J.: 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, pp. 483\u2013499. Springer (2016)","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"1441_CR34","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7103\u20137112 (2018)","DOI":"10.1109\/CVPR.2018.00742"},{"key":"1441_CR35","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: 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 (2017)","DOI":"10.1109\/CVPR.2017.143"},{"key":"1441_CR36","doi-asserted-by":"crossref","unstructured":"Li, J., Bian, S., Zeng, A., Wang, C., Pang, B., Liu, W., Lu, C.: Human pose regression with residual log-likelihood estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11025\u201311034 (2021)","DOI":"10.1109\/ICCV48922.2021.01084"},{"key":"1441_CR37","unstructured":"Newell, A., Huang, Z., Deng, J.: Associative embedding: end-to-end learning for joint detection and grouping. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"1441_CR38","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: 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 (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"issue":"4","key":"1441_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3524497","volume":"55","author":"W Liu","year":"2022","unstructured":"Liu, W., Bao, Q., Sun, Y., Mei, T.: Recent advances of monocular 2d and 3d human pose estimation: a deep learning perspective. ACM Comput. Surv. 55(4), 1\u201341 (2022)","journal-title":"ACM Comput. Surv."},{"key":"1441_CR40","doi-asserted-by":"crossref","unstructured":"Liu, R., Shen, J., Wang, H., Chen, C., Cheung, S.-C., Asari, V.: Attention mechanism exploits temporal contexts: Real-time 3d human pose reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5064\u20135073 (2020)","DOI":"10.1109\/CVPR42600.2020.00511"},{"key":"1441_CR41","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 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 (2019)","DOI":"10.1109\/CVPR.2019.00794"},{"key":"1441_CR42","doi-asserted-by":"crossref","unstructured":"Tang, Z., Qiu, Z., Hao, Y., Hong, R., Yao, T.: 3d human pose estimation with spatio-temporal criss-cross attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4790\u20134799 (2023)","DOI":"10.1109\/CVPR52729.2023.00464"},{"key":"1441_CR43","doi-asserted-by":"crossref","unstructured":"Yin, W., Lu, P., Zhao, Z., Peng, X.: Yes, \u201cattention is all you need\", for exemplar based colorization. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2243\u20132251 (2021)","DOI":"10.1145\/3474085.3475385"},{"key":"1441_CR44","first-page":"15908","volume":"34","author":"K Han","year":"2021","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. Adv. Neural. Inf. Process. Syst. 34, 15908\u201315919 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1441_CR45","doi-asserted-by":"crossref","unstructured":"Ma, H., Lu, K., Xue, J., Niu, Z., Gao, P.: Local to global transformer for video based 3d human pose estimation. In: 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICMEW56448.2022.9859482"},{"key":"1441_CR46","doi-asserted-by":"crossref","unstructured":"Tran, T.-D., Vo, X.-T., Nguyen, D.-L., Jo, K.-H.: Combination of deep learner network and transformer for 3d human pose estimation. In: 2022 22nd International Conference on Control, Automation and Systems (ICCAS), pp. 174\u2013178. IEEE (2022)","DOI":"10.23919\/ICCAS55662.2022.10003954"},{"issue":"5","key":"1441_CR47","doi-asserted-by":"publisher","first-page":"3260","DOI":"10.1109\/TCSVT.2023.3318557","volume":"34","author":"L Zhou","year":"2023","unstructured":"Zhou, L., Chen, Y., Wang, J.: Dual-path transformer for 3d human pose estimation. IEEE Trans. Circuits Syst. Video Technol. 34(5), 3260\u20133270 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1441_CR48","doi-asserted-by":"crossref","unstructured":"Mehraban, S., Adeli, V., Taati, B.: Motionagformer: enhancing 3d human pose estimation with a transformer-gcnformer network. arXiv preprint arXiv:2310.16288 (2023)","DOI":"10.1109\/WACV57701.2024.00677"},{"key":"1441_CR49","doi-asserted-by":"crossref","unstructured":"Xu, L., Song, Z., Wang, D., Su, J., Fang, Z., Ding, C., Gan, W., Yan, Y., Jin, X., Yang, X., : Actformer: a gan-based transformer towards general action-conditioned 3d human motion generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2228\u20132238 (2023)","DOI":"10.1109\/ICCV51070.2023.00212"},{"key":"1441_CR50","doi-asserted-by":"crossref","unstructured":"Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., Ding, Z.: 3d human pose estimation with spatial and temporal transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11656\u201311665 (2021)","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"1441_CR51","doi-asserted-by":"crossref","unstructured":"Zhao, L., Peng, X., Tian, Y., Kapadia, M., Metaxas, D.N.: 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 (2019)","DOI":"10.1109\/CVPR.2019.00354"},{"key":"1441_CR52","doi-asserted-by":"crossref","unstructured":"Liu, J., Rojas, J., Li, Y., Liang, Z., Guan, Y., Xi, N., Zhu, H.: A graph attention spatio-temporal convolutional network for 3d human pose estimation in video. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3374\u20133380 (2021). IEEE","DOI":"10.1109\/ICRA48506.2021.9561605"},{"key":"1441_CR53","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, H., Feng, R., Wu, S., Ji, S., Yang, B., Wang, X.: Deep dual consecutive network for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 525\u2013534 (2021)","DOI":"10.1109\/CVPR46437.2021.00059"},{"issue":"7","key":"1441_CR54","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: 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 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1441_CR55","doi-asserted-by":"crossref","unstructured":"Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3d human pose estimation in the wild using improved cnn supervision. In: 2017 International Conference on 3D Vision (3DV), pp. 506\u2013516. IEEE (2017)","DOI":"10.1109\/3DV.2017.00064"},{"key":"1441_CR56","doi-asserted-by":"crossref","unstructured":"Lee, K., Lee, I., Lee, S.: Propagating lstm: 3d pose estimation based on joint interdependency. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 119\u2013135 (2018)","DOI":"10.1007\/978-3-030-01234-2_8"},{"key":"1441_CR57","unstructured":"Zhang, Z.: Group graph convolutional networks for 3d human pose estimation. In: BMVC, p. 1019 (2022)"},{"key":"1441_CR58","doi-asserted-by":"crossref","unstructured":"Zou, Z., Tang, W.: Modulated graph convolutional network for 3d human pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11477\u201311487 (2021)","DOI":"10.1109\/ICCV48922.2021.01128"},{"key":"1441_CR59","unstructured":"Li, W., Liu, H., Guo, T., Ding, R., Tang, H.: Graphmlp: a graph mlp-like architecture for 3d human pose estimation. arXiv preprint arXiv:2206.06420 (2022)"},{"key":"1441_CR60","doi-asserted-by":"crossref","unstructured":"Shan, W., Lu, H., Wang, S., Zhang, X., Gao, W.: Improving robustness and accuracy via relative information encoding in 3d human pose estimation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3446\u20133454 (2021)","DOI":"10.1145\/3474085.3475504"},{"key":"1441_CR61","doi-asserted-by":"crossref","unstructured":"Einfalt, M., Ludwig, K., Lienhart, R.: Uplift and upsample: efficient 3d human pose estimation with uplifting transformers. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2903\u20132913 (2023)","DOI":"10.1109\/WACV56688.2023.00292"},{"key":"1441_CR62","doi-asserted-by":"crossref","unstructured":"Hassanin, M., Khamiss, A., Bennamoun, M., Boussaid, F., Radwan, I.: Crossformer: cross spatio-temporal transformer for 3d human pose estimation. arXiv preprint arXiv:2203.13387 (2022)","DOI":"10.2139\/ssrn.4213439"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01441-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01441-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01441-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T18:08:31Z","timestamp":1730138911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01441-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":62,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1441"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01441-6","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]},"assertion":[{"value":"26 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"246"}}