{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:40:25Z","timestamp":1780472425558,"version":"3.54.1"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012397","type":"print"},{"value":"9783030012403","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01240-3_41","type":"book-chapter","created":{"date-parts":[[2018,10,6]],"date-time":"2018-10-06T04:36:08Z","timestamp":1538800568000},"page":"679-696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":150,"title":["Learning 3D Human Pose from Structure and Motion"],"prefix":"10.1007","author":[{"given":"Rishabh","family":"Dabral","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anurag","family":"Mundhada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uday","family":"Kusupati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Safeer","family":"Afaque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arjun","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,10,5]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Akhter, I., Black, M.J.: Pose-conditioned joint angle limits for 3D human pose reconstruction. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298751"},{"key":"41_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-319-66709-6_28","volume-title":"Pattern Recognition","author":"T Alldieck","year":"2017","unstructured":"Alldieck, T., Kassubeck, M., Wandt, B., Rosenhahn, B., Magnor, M.: Optical flow-based 3D human motion estimation from monocular video. In: Roth, V., Vetter, T. (eds.) GCPR 2017. LNCS, vol. 10496, pp. 347\u2013360. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66709-6_28"},{"key":"41_CR3","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: CVPR (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"41_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-319-46454-1_34","volume-title":"Computer Vision \u2013 ECCV 2016","author":"F Bogo","year":"2016","unstructured":"Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 561\u2013578. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_34"},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Casiez, G., Roussel, N., Vogel. D.: 1 filter: a simple speed-based low-pass filter for noisy input in interactive systems. In: SIGCHI (2012)","DOI":"10.1145\/2207676.2208639"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Sminchisescu, C., Ionescu, C., Li, F.: Latent structured models for human pose estimation. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126500"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Chen, C.-H., Ramanan, D.: 3D human pose estimation $$=$$ 2D pose estimation + matching. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.610"},{"key":"41_CR8","doi-asserted-by":"publisher","first-page":"4627","DOI":"10.1109\/TIP.2013.2274748","volume":"22","author":"J Chen","year":"2013","unstructured":"Chen, J., Nie, S., Ji, Q.: Data-free prior model for upper body pose estimation and tracking. IEEE Trans. Image Process. 22, 4627\u20134639 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Synthesizing training images for boosting human 3D pose estimation. In: 3DV (2016)","DOI":"10.1109\/3DV.2016.58"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Coskun, H., Achilles, F., DiPietro, R., Navab, N., Tombari, F.: Long short-term memory Kalman filters: recurrent neural estimators for pose regularization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.589"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"41_CR13","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.cviu.2005.01.005","volume":"99","author":"L Herda","year":"2005","unstructured":"Herda, L., Urtasun, R., Fua, P.: Hierarchical implicit surface joint limits for human body tracking. Comput. Vis. Image Underst. 99, 189\u2013209 (2005)","journal-title":"Comput. Vis. Image Underst."},{"key":"41_CR14","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 TPAMI 36, 1325\u20131339 (2014)","journal-title":"IEEE TPAMI"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Jahangiri, E., Yuille, A.L.: Generating multiple diverse hypotheses for human 3D pose consistent with 2D joint detections. In: ICCV (2017)","DOI":"10.1109\/ICCVW.2017.100"},{"key":"41_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"41_CR17","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":"41_CR18","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: ICCV (2015)","DOI":"10.1109\/ICCV.2015.326"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Lin, M., Lin, L., Liang, X., Wang, K., Cheng, H.: Recurrent 3D pose sequence machines. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.588"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T., et al.: Microsoft COCO: common objects in context. arXiv preprint arXiv:1405.0312 (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"41_CR21","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. 34, 248 (2015)","journal-title":"ACM Trans. Graph."},{"key":"41_CR22","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Monocular 3D human pose estimation in the wild using improved CNN supervision. In: 3DV (2017)","DOI":"10.1109\/3DV.2017.00064"},{"key":"41_CR23","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/3072959.3073596","volume":"36","author":"D Mehta","year":"2017","unstructured":"Mehta, D.: VNect: real-time 3D human pose estimation with a single RGB camera. ACM ToG 36, 44 (2017)","journal-title":"ACM ToG"},{"key":"41_CR24","doi-asserted-by":"crossref","unstructured":"Moreno-Noguer, F.: 3D human pose estimation from a single image via distance matrix regression. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.170"},{"key":"41_CR25","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":"41_CR26","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1145\/1183287.1183291","volume":"25","author":"MJ Park","year":"2006","unstructured":"Park, M.J., Choi, M.G., Shinagawa, Y., Shin, S.Y.: Video-guided motion synthesis using example motions. ACM ToG 25, 1327\u20131359 (2006)","journal-title":"ACM ToG"},{"key":"41_CR27","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: CVPR (2017)","DOI":"10.1109\/CVPR.2017.139"},{"key":"41_CR28","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":"41_CR29","unstructured":"Rogez, G., Schmid, C.: MoCap-guided data augmentation for 3D pose estimation in the wild. In: NIPS (2016)"},{"key":"41_CR30","doi-asserted-by":"crossref","unstructured":"Rogez, G., Weinzaepfel, P., Schmid, C.: LCR-Net: localization-classification-regression for human pose. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.134"},{"key":"41_CR31","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. ArXiv e-prints (2014)"},{"key":"41_CR32","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":"41_CR33","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1177\/0278364903022006003","volume":"22","author":"C Sminchisescu","year":"2003","unstructured":"Sminchisescu, C., Triggs, B.: Estimating articulated human motion with covariance scaled sampling. Int. J. Robot. Res. 22, 371\u2013391 (2003)","journal-title":"Int. J. Robot. Res."},{"key":"41_CR34","doi-asserted-by":"crossref","unstructured":"Sun, X., Shang, J., Liang, S., Wei, Y.: Compositional human pose regression. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.284"},{"key":"41_CR35","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)","DOI":"10.1109\/CVPR.2017.603"},{"key":"41_CR36","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.cviu.2006.08.006","volume":"104","author":"R Urtasun","year":"2006","unstructured":"Urtasun, R., Fleet, D.J., Fua, P.: Temporal motion models for monocular and multiview 3D human body tracking. Comput. Vis. Image Underst. 104, 157\u2013177 (2006)","journal-title":"Comput. Vis. Image Underst."},{"key":"41_CR37","doi-asserted-by":"crossref","unstructured":"Varol, G., et al.: Learning from synthetic humans. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.492"},{"key":"41_CR38","first-page":"42","volume":"29","author":"X Wei","year":"2010","unstructured":"Wei, X., Chai, J.: VideoMocap: modeling physically realistic human motion from monocular video sequences. ACM ToG 29, 42 (2010)","journal-title":"ACM ToG"},{"key":"41_CR39","doi-asserted-by":"crossref","unstructured":"Nie, B.X., Wei, P., Zhu, S.-C.: Monocular 3D human pose estimation by predicting depth on joints. In: ICCV, October 2017","DOI":"10.1109\/ICCV.2017.373"},{"key":"41_CR40","doi-asserted-by":"crossref","unstructured":"Yasin, H., Iqbal, U., Kruger, B., Weber, A., Gall, J.: A dual-source approach for 3D pose estimation from a single image. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.535"},{"key":"41_CR41","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: ICCV (2017)","DOI":"10.1109\/ICCV.2017.51"},{"key":"41_CR42","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":"41_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhu, M., Derpanis, K., Daniilidis, K.: Sparseness meets deepness: 3D human pose estimation from monocular video. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.537"},{"key":"41_CR44","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"Z-H Zhou","year":"2017","unstructured":"Zhou, Z.-H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5, 44\u201353 (2017)","journal-title":"Natl. Sci. Rev."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01240-3_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,6]],"date-time":"2022-10-06T00:07:04Z","timestamp":1665014824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01240-3_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012397","9783030012403"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01240-3_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"5 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}