{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:16:16Z","timestamp":1742984176044,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030161415"},{"type":"electronic","value":"9783030161422"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-16142-2_31","type":"book-chapter","created":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T02:50:37Z","timestamp":1554346237000},"page":"400-411","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-scale Recalibrated Approach for 3D Human Pose Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1436-795X","authenticated-orcid":false,"given":"Ziwei","family":"Xie","sequence":"first","affiliation":[]},{"given":"Hailun","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Chunyan","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,20]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2016.09.002","volume":"152","author":"N Sarafianos","year":"2016","unstructured":"Sarafianos, N., Boteanu, B., Ionescu, B., Kakadiaris, I.A.: 3D human pose estimation: a review of the literature and analysis of covariates. Comput. Vis. Image Underst. 152, 1\u201320 (2016)","journal-title":"Comput. Vis. Image Underst."},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 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 (2014)","DOI":"10.1109\/CVPR.2014.471"},{"issue":"1\u20132","key":"31_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s11263-009-0273-6","volume":"87","author":"L Sigal","year":"2010","unstructured":"Sigal, L., Balan, A.O., Black, M.J.: HUMANEVA: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int. J. Comput. Vis. 87(1\u20132), 4 (2010)","journal-title":"Int. J. Comput. Vis."},{"issue":"7","key":"31_CR4","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.: 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 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Moreno-Noguer, F.: 3D human pose estimation from a single image via distance matrix regression. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1561\u20131570. IEEE (2017)","DOI":"10.1109\/CVPR.2017.170"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Chen, C.-H., Ramanan, D.: 3D human pose estimation = 2D pose estimation + matching. In: CVPR, p. 6 (2017)","DOI":"10.1109\/CVPR.2017.610"},{"key":"31_CR7","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: IEEE International Conference on Computer Vision, p. 3 (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"31_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-642-33765-9_41","volume-title":"Computer Vision \u2013 ECCV 2012","author":"V Ramakrishna","year":"2012","unstructured":"Ramakrishna, V., Kanade, T., Sheikh, Y.: Reconstructing 3D human pose from 2D image landmarks. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 573\u2013586. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33765-9_41"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image\u00a03D human pose. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1263\u20131272. IEEE (2017)","DOI":"10.1109\/CVPR.2017.139"},{"key":"31_CR10","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":"31_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/978-3-319-16808-1_23","volume-title":"Computer Vision \u2013 ACCV 2014","author":"S Li","year":"2015","unstructured":"Li, S., Chan, A.B.: 3D human pose estimation from monocular images with deep convolutional neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 332\u2013347. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16808-1_23"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Li, S., Zhang, W., Chan, A.B.: Maximum-margin structured learning with deep networks for 3D human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2848\u20132856 (2015)","DOI":"10.1109\/ICCV.2015.326"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Varol, G., et al.: Learning from synthetic humans. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (2017)","DOI":"10.1109\/CVPR.2017.492"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., Padoy, N.: A multi-view RGB-D approach for human pose estimation in operating rooms. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 363\u2013372. IEEE (2017)","DOI":"10.1109\/WACV.2017.47"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Sun, X., Shang, J., Liang, S., Wei, Y.: Compositional human pose regression. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.284"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y.: Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.51"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint: arXiv:1709.01507 (2017)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, L., Qiao, S., Zhang, Y., Zhang, W., Yuille, A.L.: Multi-scale spatially-asymmetric recalibration for image classification. arXiv preprint: arXiv:1804.00787 (2018)","DOI":"10.1007\/978-3-030-01261-8_31"},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"31_CR20","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1006\/cviu.2000.0897","volume":"81","author":"TB Moeslund","year":"2001","unstructured":"Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231\u2013268 (2001)","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"31_CR21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/3072959.3073596","volume":"36","author":"D Mehta","year":"2017","unstructured":"Mehta, D., et al.: VNect: real-time 3D human pose estimation with a single RGB camera. ACM Trans. Graph. (TOG) 36(4), 44 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724\u20134732 (2016)","DOI":"10.1109\/CVPR.2016.511"},{"key":"31_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Akhter, I., Black, M.J.: Pose-conditioned joint angle limits for 3D human pose reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1446\u20131455 (2015)","DOI":"10.1109\/CVPR.2015.7298751"},{"key":"31_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-319-49409-8_17","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"X Zhou","year":"2016","unstructured":"Zhou, X., Sun, X., Zhang, W., Liang, S., Wei, Y.: Deep kinematic pose regression. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 186\u2013201. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_17"},{"key":"31_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346\u2013361. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. arXiv preprint: arXiv:1504.08083 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"31_CR28","doi-asserted-by":"crossref","unstructured":"Xie, L., Zheng, L., Wang, J., Yuille, A.L., Tian, Q.: Interactive: inter-layer activeness propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 270\u2013279 (2016)","DOI":"10.1109\/CVPR.2016.36"},{"key":"31_CR29","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640\u20133649 (2016)","DOI":"10.1109\/CVPR.2016.396"},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Quattoni, A., Torras, C., Moreno-Noguer, F.: A joint model for 2D and 3D pose estimation from a single image. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3634\u20133641. IEEE (2013)","DOI":"10.1109\/CVPR.2013.466"},{"key":"31_CR31","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint: arXiv:1502.03167 (2015)"},{"key":"31_CR32","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807\u2013814 (2010)"},{"key":"31_CR33","doi-asserted-by":"crossref","unstructured":"Tome, D., Russell, C., Agapito, L.: Lifting from the deep: convolutional 3D pose estimation from a single image. In: CVPR 2017 Proceedings, pp. 2500\u20132509 (2017)","DOI":"10.1109\/CVPR.2017.603"},{"key":"31_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhu, M., Pavlakos, G., Leonardos, S., Derpanis, K.G., Daniilidis, K.: MonoCap: monocular human motion capture using a CNN coupled with a geometric prior. IEEE Trans. Pattern Anal. Mach. Intell. (2018)","DOI":"10.1109\/TPAMI.2018.2816031"},{"key":"31_CR35","doi-asserted-by":"crossref","unstructured":"Mehta, D., Rhodin, H., Casas, D., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3D human pose estimation using transfer learning and improved CNN supervision. arXiv preprint: arXiv:1611.09813 (2016)","DOI":"10.1109\/3DV.2017.00064"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-16142-2_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:21:38Z","timestamp":1709814098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-16142-2_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030161415","9783030161422"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-16142-2_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macau","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.pakdd2019.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conf. Man. Toolkit CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"542","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"137","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.79","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.85","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In addition, there were 31 PAKDD 2019 Workshops' papers accepted for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}