{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:43:48Z","timestamp":1742913828696,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030110116"},{"type":"electronic","value":"9783030110123"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-11012-3_31","type":"book-chapter","created":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T17:50:19Z","timestamp":1548697819000},"page":"395-408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Semi-supervised Data Augmentation Approach Using 3D Graphical Engines"],"prefix":"10.1007","author":[{"given":"Shuangjun","family":"Liu","sequence":"first","affiliation":[]},{"given":"Sarah","family":"Ostadabbas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,29]]},"reference":[{"key":"31_CR1","unstructured":"MeshLab. http:\/\/www.meshlab.net\/. Accessed 2018"},{"key":"31_CR2","unstructured":"CMU graphics lab motion capture database (2018). http:\/\/mocap.cs.cmu.edu\/"},{"key":"31_CR3","unstructured":"Skanect 3D Scanning Software By Occipital. http:\/\/skanect.occipital.com\/. Accessed 2018"},{"key":"31_CR4","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: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, June 2014","DOI":"10.1109\/CVPR.2014.471"},{"issue":"3","key":"31_CR5","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1145\/1073204.1073207","volume":"24","author":"D Anguelov","year":"2005","unstructured":"Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: SCAPE: shape completion and animation of people. ACM Trans. Graph. 24(3), 408\u2013416 (2005)","journal-title":"ACM Trans. Graph."},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Aubry, M., Maturana, D., Efros, A.A., Russell, B.C., Sivic, J.: Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3762\u20133769 (2014)","DOI":"10.1109\/CVPR.2014.487"},{"key":"31_CR7","unstructured":"Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 17\u201336 (2012)"},{"key":"31_CR8","unstructured":"Bengio, Y., et al.: Deep learners benefit more from out-of-distribution examples. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 164\u2013172 (2011)"},{"key":"31_CR9","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-7908-2604-3_16","volume-title":"Proceedings of COMPSTAT","author":"L Bottou","year":"2010","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT, pp. 177\u2013186. Physica-Verlag, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16"},{"key":"31_CR10","unstructured":"Caruana, R.: Learning many related tasks at the same time with backpropagation. In: Advances in Neural Information Processing Systems, pp. 657\u2013664 (1995)"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Synthesizing training images for boosting human 3D pose estimation. In: 2016 Fourth International Conference on 3D Vision, 3DV, pp. 479\u2013488 (2016)","DOI":"10.1109\/3DV.2016.58"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"31_CR13","volume-title":"Introduction to Robotics: Mechanics and Control","author":"JJ Craig","year":"2005","unstructured":"Craig, J.J.: Introduction to Robotics: Mechanics and Control, vol. 3. Pearson Prentice Hall, Upper Saddle River (2005)"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"31_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-46493-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Du","year":"2016","unstructured":"Du, Y., et al.: Marker-less 3D human motion capture with monocular image sequence and height-maps. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 20\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_2"},{"issue":"2","key":"31_CR16","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. arXiv preprint arXiv:1605.06457 (2016)","DOI":"10.1109\/CVPR.2016.470"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Ghezelghieh, M.F., Kasturi, R., Sarkar, S.: Learning camera viewpoint using CNN to improve 3D body pose estimation. In: 2016 Fourth International Conference on 3D Vision, 3DV, pp. 685\u2013693 (2016)","DOI":"10.1109\/3DV.2016.75"},{"key":"31_CR19","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2066\u20132073 (2012)"},{"key":"31_CR20","doi-asserted-by":"publisher","unstructured":"Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: Proceedings of the British Machine Vision Conference (2010). https:\/\/doi.org\/10.5244\/C.24.12","DOI":"10.5244\/C.24.12"},{"key":"31_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-54536-8","volume-title":"Introduction to Humanoid Robotics","author":"S Kajita","year":"2014","unstructured":"Kajita, S., Hirukawa, H., Harada, K., Yokoi, K.: Introduction to Humanoid Robotics, vol. 101. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-642-54536-8"},{"key":"31_CR22","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"31_CR23","unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562\u2013570 (2015)"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Liebelt, J., Schmid, C.: Multi-view object class detection with a 3D geometric model. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1688\u20131695 (2010)","DOI":"10.1109\/CVPR.2010.5539836"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Liu, S., Yin, Y., Ostadabbas, S.: In-bed pose estimation: deep learning with shallow dataset. arXiv preprint arXiv:1711.01005 (2018)","DOI":"10.1109\/JTEHM.2019.2892970"},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Marin, J., V\u00e1zquez, D., Ger\u00f3nimo, D., L\u00f3pez, A.M.: Learning appearance in virtual scenarios for pedestrian detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 137\u2013144 (2010)","DOI":"10.1109\/CVPR.2010.5540218"},{"key":"31_CR27","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_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/978-3-540-88688-4_32","volume-title":"Computer Vision \u2013 ECCV 2008","author":"R Okada","year":"2008","unstructured":"Okada, R., Soatto, S.: Relevant feature selection for human pose estimation and localization in cluttered images. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 434\u2013445. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88688-4_32"},{"key":"31_CR29","doi-asserted-by":"crossref","unstructured":"Pishchulin, L., Jain, A., Andriluka, M., Thorm\u00e4hlen, T., Schiele, B.: Articulated people detection and pose estimation: reshaping the future. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 3178\u20133185 (2012)","DOI":"10.1109\/CVPR.2012.6248052"},{"key":"31_CR30","unstructured":"Qiu, W.: Generating human images and ground truth using computer graphics. Ph.D. thesis. University of California, Los Angeles (2016)"},{"key":"31_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/978-3-319-24947-6_34","volume-title":"Pattern Recognition","author":"J Romero","year":"2015","unstructured":"Romero, J., Loper, M., Black, M.J.: FlowCap: 2D human pose from optical flow. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 412\u2013423. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24947-6_34"},{"key":"31_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"31_CR33","doi-asserted-by":"crossref","unstructured":"Stark, M., Goesele, M., Schiele, B.: Back to the future: learning shape models from 3D CAD data. In: BMVC, vol. 2, no. 4, p. 5 (2010)","DOI":"10.5244\/C.24.106"},{"key":"31_CR34","doi-asserted-by":"crossref","unstructured":"Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2686\u20132694 (2015)","DOI":"10.1109\/ICCV.2015.308"},{"key":"31_CR35","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., Saenko, K.: Correlation alignment for unsupervised domain adaptation. arXiv preprint arXiv:1612.01939 (2016)","DOI":"10.5244\/C.29.24"},{"key":"31_CR36","unstructured":"Sun, B., Peng, X., Saenko, K.: Generating large scale image datasets from 3D CAD models. In: CVPR 2015 Workshop on the Future of Datasets in Vision (2015)"},{"key":"31_CR37","unstructured":"Sun, M., Su, H., Savarese, S., Fei-Fei, L.: A multi-view probabilistic model for 3D object classes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1247\u20131254 (2009)"},{"key":"31_CR38","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_CR39","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_CR40","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328 (2014)"},{"key":"31_CR41","unstructured":"Yu, F., Zhang, Y., Song, S., Seff, A., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)"},{"key":"31_CR42","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11012-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T03:15:01Z","timestamp":1674875701000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11012-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110116","9783030110123"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11012-3_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":"29 January 2019","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"}]}}