{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:04:40Z","timestamp":1784268280312,"version":"3.55.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585648","type":"print"},{"value":"9783030585655","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58565-5_21","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T12:03:19Z","timestamp":1605096199000},"page":"344-359","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":132,"title":["CLOTH3D: Clothed 3D Humans"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6632-1902","authenticated-orcid":false,"given":"Hugo","family":"Bertiche","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7384-5712","authenticated-orcid":false,"given":"Meysam","family":"Madadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0617-8873","authenticated-orcid":false,"given":"Sergio","family":"Escalera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"21_CR1","unstructured":"Carnegie-Mellon Mocap Database. http:\/\/mocap.cs.cmu.edu\/"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Detailed human avatars from monocular video. In: 2018 International Conference on 3D Vision (3DV), pp. 98\u2013109. IEEE (2018)","DOI":"10.1109\/3DV.2018.00022"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Alldieck, T., Pons-Moll, G., Theobalt, C., Magnor, M.: Tex2Shape: detailed full human body geometry from a single image. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2293\u20132303 (2019)","DOI":"10.1109\/ICCV.2019.00238"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Amberg, B., Romdhani, S., Vetter, T.: Optimal step nonrigid ICP algorithms for surface registration. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/CVPR.2007.383165"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Bhatnagar, B.L., Tiwari, G., Theobalt, C., Pons-Moll, G.: Multi-garment net: learning to dress 3D people from images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5420\u20135430 (2019)","DOI":"10.1109\/ICCV.2019.00552"},{"issue":"4","key":"21_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Sig. Process. Mag. 34(4), 18\u201342 (2017)","journal-title":"IEEE Sig. Process. Mag."},{"key":"21_CR7","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems, pp. 3844\u20133852 (2016)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Q., Gong, S., Zhu, X.: Multi-task curriculum transfer deep learning of clothing attributes. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 520\u2013529. IEEE (2017)","DOI":"10.1109\/WACV.2017.64"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 209\u2013216. ACM Press\/Addison-Wesley Publishing Co. (1997)","DOI":"10.1145\/258734.258849"},{"issue":"4","key":"21_CR10","doi-asserted-by":"publisher","first-page":"35:1","DOI":"10.1145\/2185520.2185531","volume":"31","author":"P Guan","year":"2012","unstructured":"Guan, P., Reiss, L., Hirshberg, D.A., Weiss, A., Black, M.J.: Drape: dressing any person. ACM Trans. Graph. 31(4), 35:1\u201335:10 (2012)","journal-title":"ACM Trans. Graph."},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Gundogdu, E., Constantin, V., Seifoddini, A., Dang, M., Salzmann, M., Fua, P.: GarNet: a two-stream network for fast and accurate 3D cloth draping. In: IEEE International Conference on Computer Vision (ICCV). IEEE, October 2019","DOI":"10.1109\/ICCV.2019.00883"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Lahner, Z., Cremers, D., Tung, T.: DeepWrinkles: accurate and realistic clothing modeling. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 667\u2013684 (2018)","DOI":"10.1007\/978-3-030-01225-0_41"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Lin, K., Yang, H.F., Liu, K.H., Hsiao, J.H., Chen, C.S.: Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 499\u2013502. ACM (2015)","DOI":"10.1145\/2671188.2749318"},{"issue":"6","key":"21_CR14","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1145\/2661229.2661273","volume":"33","author":"M Loper","year":"2014","unstructured":"Loper, M., Mahmood, N., Black, M.J.: Mosh: motion and shape capture from sparse markers. ACM Trans. Graph. (TOG) 33(6), 220 (2014)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 34(6), 248:1\u2013248:16 (2015)","DOI":"10.1145\/2816795.2818013"},{"issue":"6","key":"21_CR16","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. (TOG) 34(6), 248 (2015)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"21_CR17","unstructured":"Ma, Q., Tang, S., Pujades, S., Pons-Moll, G., Ranjan, A., Black, M.J.: Dressing 3D humans using a conditional Mesh-VAE-GAN. arXiv preprint arXiv:1907.13615 (2019)"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"von Marcard, T., Henschel, R., Black, M., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3D human pose in the wild using IMUs and a moving camera. In: European Conference on Computer Vision (ECCV), September 2018","DOI":"10.1007\/978-3-030-01249-6_37"},{"key":"21_CR19","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014\u20132023 (2016)"},{"key":"21_CR20","unstructured":"Nikolenko, S.I.: Synthetic data for deep learning. arXiv abs\/1909.11512 (2019)"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Patel, C., Liao, Z., Pons-Moll, G.: TailorNet: predicting clothing in 3D as a function of human pose, shape and garment style. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7365\u20137375 (2020)","DOI":"10.1109\/CVPR42600.2020.00739"},{"issue":"4","key":"21_CR22","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1145\/3072959.3073711","volume":"36","author":"G Pons-Moll","year":"2017","unstructured":"Pons-Moll, G., Pujades, S., Hu, S., Black, M.J.: ClothCap: seamless 4D clothing capture and retargeting. ACM Trans. Graph. (TOG) 36(4), 73 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Pumarola, A., Goswami, V., Vicente, F., De la Torre, F., Moreno-Noguer, F.: Unsupervised image-to-video clothing transfer. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019","DOI":"10.1109\/ICCVW.2019.00394"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Pumarola, A., Sanchez-Riera, J., Choi, G., Sanfeliu, A., Moreno-Noguer, F.: 3Dpeople: modeling the geometry of dressed humans. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2242\u20132251 (2019)","DOI":"10.1109\/ICCV.2019.00233"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.352"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Santesteban, I., Otaduy, M.A., Casas, D.: Learning-based animation of clothing for virtual try-on. In: Computer Graphics Forum, vol. 38, pp. 355\u2013366. Wiley Online Library (2019)","DOI":"10.1111\/cgf.13643"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Shin, D., Chen, Y.: Deep garment image matting for a virtual try-on system. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019","DOI":"10.1109\/ICCVW.2019.00384"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Varol, G., et al.: Learning from synthetic humans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 109\u2013117 (2017)","DOI":"10.1109\/CVPR.2017.492"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Wang, T.Y., Ceylan, D., Popovic, J., Mitra, N.J.: Learning a shared shape space for multimodal garment design. arXiv preprint arXiv:1806.11335 (2018)","DOI":"10.1145\/3272127.3275074"},{"issue":"6","key":"21_CR30","first-page":"1","volume":"38","author":"TY Wang","year":"2019","unstructured":"Wang, T.Y., Shao, T., Fu, K., Mitra, N.J.: Learning an intrinsic garment space for interactive authoring of garment animation. ACM Trans. Graph. (TOG) 38(6), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"21_CR31","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019)"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Yang, J., Franco, J.S., H\u00e9troy-Wheeler, F., Wuhrer, S.: Analyzing clothing layer deformation statistics of 3D human motions. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 237\u2013253 (2018)","DOI":"10.1007\/978-3-030-01234-2_15"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Yu, T., et al.: SimulCap: single-view human performance capture with cloth simulation. arXiv preprint arXiv:1903.06323 (2019)","DOI":"10.1109\/CVPR.2019.00565"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Yuan, Y.J., Lai, Y.K., Yang, J., Fu, H., Gao, L.: Mesh variational autoencoders with edge contraction pooling. arXiv preprint arXiv:1908.02507 (2019)","DOI":"10.1109\/CVPRW50498.2020.00145"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, C., Pujades, S., Black, M.J., Pons-Moll, G.: Detailed, accurate, human shape estimation from clothed 3D scan sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4191\u20134200 (2017)","DOI":"10.1109\/CVPR.2017.582"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58565-5_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:07:26Z","timestamp":1731283646000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58565-5_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585648","9783030585655"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58565-5_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}