{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T06:02:55Z","timestamp":1770530575901,"version":"3.49.0"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200670","type":"print"},{"value":"9783031200687","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20068-7_13","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T08:06:38Z","timestamp":1668067598000},"page":"222-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AutoAvatar: Autoregressive Neural Fields for\u00a0Dynamic Avatar Modeling"],"prefix":"10.1007","author":[{"given":"Ziqian","family":"Bai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timur","family":"Bagautdinov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Romero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Zollh\u00f6fer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunsuke","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Alldieck, T., Xu, H., Sminchisescu, C.: imGHUM: implicit generative models of 3D human shape and articulated pose. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 5461\u20135470 (2021)","DOI":"10.1109\/ICCV48922.2021.00541"},{"key":"13_CR2","unstructured":"Allen, B., Curless, B., Popovi\u0107, Z., Hertzmann, A.: Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis. In: Proceedings of the 2006 ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, pp. 147\u2013156 (2006)"},{"issue":"3","key":"13_CR3","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. (TOG) 24(3), 408\u2013416 (2005)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"13_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459850","volume":"40","author":"T Bagautdinov","year":"2021","unstructured":"Bagautdinov, T., et al.: Driving-signal aware full-body avatars. ACM Trans. Graph. (TOG) 40(4), 1\u201317 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"13_CR5","unstructured":"Bhat, K.S., Twigg, C.D., Hodgins, J.K., Khosla, P., Popovic, Z., Seitz, S.M.: Estimating cloth simulation parameters from video (2003)"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Bogo, F., Romero, J., Loper, M., Black, M.J.: Faust: dataset and evaluation for 3D mesh registration. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 3794\u20133801 (2014)","DOI":"10.1109\/CVPR.2014.491"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Bogo, F., Romero, J., Pons-Moll, G., Black, M.J.: Dynamic faust: registering human bodies in motion. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 6233\u20136242 (2017)","DOI":"10.1109\/CVPR.2017.591"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X., Zheng, Y., Black, M.J., Hilliges, O., Geiger, A.: Snarf: differentiable forward skinning for animating non-rigid neural implicit shapes. In: Proceedings of International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01139"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 5939\u20135948. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00609"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/978-3-030-58571-6_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Deng","year":"2020","unstructured":"Deng, B., et al.: NASA neural articulated shape approximation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 612\u2013628. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58571-6_36"},{"key":"13_CR11","unstructured":"Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. In: Proceedings of the 37th International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, vol. 119, pp. 3789\u20133799. PMLR (2020)"},{"issue":"4","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459749","volume":"40","author":"M Habermann","year":"2021","unstructured":"Habermann, M., Liu, L., Xu, W., Zollhoefer, M., Pons-Moll, G., Theobalt, C.: Real-time deep dynamic characters. ACM Trans. Graph. (TOG) 40(4), 1\u201316 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"2","key":"13_CR13","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1111\/j.1467-8659.2009.01373.x","volume":"28","author":"N Hasler","year":"2009","unstructured":"Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.: A statistical model of human pose and body shape. Comput. Graph. Forum 28(2), 337\u2013346 (2009)","journal-title":"Comput. Graph. Forum"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Holden, D., Duong, B.C., Datta, S., Nowrouzezahrai, D.: Subspace neural physics: fast data-driven interactive simulation. In: Proceedings of the 18th Annual ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, pp. 1\u201312 (2019)","DOI":"10.1145\/3309486.3340245"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3D human dynamics from video. In: Proceedings of Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00576"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Kim, M., et al.: Data-driven physics for human soft tissue animation. ACM Trans. Graph. (TOG) 36(4), 54:1\u201354:12 (2017)","DOI":"10.1145\/3072959.3073685"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-319-49409-8_7","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"C Lea","year":"2016","unstructured":"Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: a unified approach to action segmentation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 47\u201354. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_7"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Li, R., Yang, S., Ross, D.A., Kanazawa, A.: AI choreographer: music conditioned 3D dance generation with AIST++. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 13401\u201313412 (2021)","DOI":"10.1109\/ICCV48922.2021.01315"},{"issue":"4","key":"13_CR19","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/3386569.3392422","volume":"39","author":"HY Ling","year":"2020","unstructured":"Ling, H.Y., Zinno, F., Cheng, G., Van De Panne, M.: Character controllers using motion VAEs. ACM Trans. Graph. (TOG) 39(4), 40\u20131 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"6","key":"13_CR20","first-page":"1","volume":"40","author":"L Liu","year":"2021","unstructured":"Liu, L., Habermann, M., Rudnev, V., Sarkar, K., Gu, J., Theobalt, C.: Neural actor: neural free-view synthesis of human actors with pose control. ACM Trans. Graph. (TOG) 40(6), 1\u201316 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"6","key":"13_CR21","doi-asserted-by":"publisher","first-page":"1","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), 1\u201313 (2014)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"13_CR22","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. (TOG) 34(6), 248:1\u2013248:16 (2015)","DOI":"10.1145\/2816795.2818013"},{"issue":"4","key":"13_CR23","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Comput. Graph. 21(4), 163\u2013169 (1987)","journal-title":"ACM Siggraph Comput. Graph."},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Ma, Q., Saito, S., Yang, J., Tang, S., Black, M.J.: SCALE: modeling clothed humans with a surface codec of articulated local elements. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), June 2021","DOI":"10.1109\/CVPR46437.2021.01582"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Ma, Q., Yang, J., Tang, S., Black, M.J.: The power of points for modeling humans in clothing. In: Proceedings of International Conference on Computer Vision (ICCV), October 2021","DOI":"10.1109\/ICCV48922.2021.01079"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: AMASS: archive of motion capture as surface shapes. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 5442\u20135451 (2019)","DOI":"10.1109\/ICCV.2019.00554"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 2891\u20132900 (2017)","DOI":"10.1109\/CVPR.2017.497"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Mescheder, L.M., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 4460\u20134470. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Mihajlovic, M., Zhang, Y., Black, M.J., Tang, S.: LEAP: learning articulated occupancy of people. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), June 2021","DOI":"10.1109\/CVPR46437.2021.01032"},{"key":"13_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: learning implicit 3D representations without 3D supervision. In: Proceedings of Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"13_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/978-3-030-58539-6_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"AAA Osman","year":"2020","unstructured":"Osman, A.A.A., Bolkart, T., Black, M.J.: STAR: sparse trained articulated human body regressor. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 598\u2013613. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_36"},{"issue":"11","key":"13_CR33","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1103\/PhysRevLett.64.1196","volume":"64","author":"E Ott","year":"1990","unstructured":"Ott, E., Grebogi, C., Yorke, J.A.: Controlling chaos. Phys. Rev. Lett. 64(11), 1196 (1990)","journal-title":"Phys. Rev. Lett."},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Palafox, P., Bo\u017ei\u010d, A., Thies, J., Nie\u00dfner, M., Dai, A.: NPMS: neural parametric models for 3D deformable shapes. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 12695\u201312705 (2021)","DOI":"10.1109\/ICCV48922.2021.01246"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R.A., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 165\u2013174. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Peng, S., et al.: Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 9054\u20139063 (2021)","DOI":"10.1109\/CVPR46437.2021.00894"},{"key":"13_CR37","unstructured":"Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., Battaglia, P.: Learning mesh-based simulation with graph networks. In: International Conference on Learning Representations (2021)"},{"issue":"4","key":"13_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2766993","volume":"34","author":"G Pons-Moll","year":"2015","unstructured":"Pons-Moll, G., Romero, J., Mahmood, N., Black, M.J.: Dyna: a model of dynamic human shape in motion. ACM Trans. Graph. (TOG) 34(4), 1\u201314 (2015)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Prokudin, S., Lassner, C., Romero, J.: Efficient learning on point clouds with basis point sets. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 4332\u20134341 (2019)","DOI":"10.1109\/ICCV.2019.00443"},{"key":"13_CR40","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013660 (2017)"},{"key":"13_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Saito, S., Yang, J., Ma, Q., Black, M.J.: SCANimate: weakly supervised learning of skinned clothed avatar networks. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), June 2021","DOI":"10.1109\/CVPR46437.2021.00291"},{"key":"13_CR43","unstructured":"Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., Battaglia, P.: Learning to simulate complex physics with graph networks. In: International Conference on Machine Learning, pp. 8459\u20138468. PMLR (2020)"},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Santesteban, I., Garces, E., Otaduy, M.A., Casas, D.: SoftSMPL: data-driven modeling of nonlinear soft-tissue dynamics for parametric humans. In: Computer Graphics Forum, vol. 39, pp. 65\u201375. Wiley Online Library (2020)","DOI":"10.1111\/cgf.13912"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"Sifakis, E., Barbic, J.: Fem simulation of 3D deformable solids: a practitioner\u2019s guide to theory, discretization and model reduction. In: ACM SIGGRAPH 2012 Courses, pp. 1\u201350 (2012)","DOI":"10.1145\/2343483.2343501"},{"issue":"4","key":"13_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459883","volume":"40","author":"SG Srinivasan","year":"2021","unstructured":"Srinivasan, S.G., Wang, Q., Rojas, J., Kl\u00e1r, G., Kavan, L., Sifakis, E.: Learning active quasistatic physics-based models from data. ACM Trans. Graph. (TOG) 40(4), 1\u201314 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Tiwari, G., Sarafianos, N., Tung, T., Pons-Moll, G.: Neural-gif: neural generalized implicit functions for animating people in clothing. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 11708\u201311718 (2021)","DOI":"10.1109\/ICCV48922.2021.01150"},{"key":"13_CR48","unstructured":"Tsuchida, S., Fukayama, S., Hamasaki, M., Goto, M.: AIST dance video database: multi-genre, multi-dancer, and multi-camera database for dance information processing. In: ISMIR, vol. 1, p. 6 (2019)"},{"issue":"4","key":"13_CR49","first-page":"1","volume":"30","author":"H Wang","year":"2011","unstructured":"Wang, H., O\u2019Brien, J.F., Ramamoorthi, R.: Data-driven elastic models for cloth: modeling and measurement. ACM Trans. Graph. (TOG) 30(4), 1\u201312 (2011)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"13_CR50","unstructured":"Wang, S., Mihajlovic, M., Ma, Q., Geiger, A., Tang, S.: Metaavatar: learning animatable clothed human models from few depth images. In: Proceedings of Advances in Neural Information Processing Systems (NeurIPS), vol. 34 (2021)"},{"issue":"6","key":"13_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478513.3480545","volume":"40","author":"D Xiang","year":"2021","unstructured":"Xiang, D., et al.: Modeling clothing as a separate layer for an animatable human avatar. ACM Trans. Graph. (TOG) 40(6), 1\u201315 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"13_CR52","unstructured":"Xie, Y., et al.: Neural fields in visual computing and beyond. arXiv preprint arXiv:2111.11426 (2021)"},{"key":"13_CR53","doi-asserted-by":"crossref","unstructured":"Xu, H., Bazavan, E.G., Zanfir, A., Freeman, W.T., Sukthankar, R., Sminchisescu, C.: GHUM & GHUML: generative 3D human shape and articulated pose models. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 6183\u20136192. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00622"},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"Yang, S., Liang, J., Lin, M.C.: Learning-based cloth material recovery from video. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 4393\u20134403. IEEE Computer Society (2017)","DOI":"10.1109\/ICCV.2017.470"},{"key":"13_CR55","doi-asserted-by":"crossref","unstructured":"Zakharkin, I., Mazur, K., Grigorev, A., Lempitsky, V.: Point-based modeling of human clothing. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 14718\u201314727 (2021)","DOI":"10.1109\/ICCV48922.2021.01445"},{"key":"13_CR56","doi-asserted-by":"crossref","unstructured":"Zheng, M., Zhou, Y., Ceylan, D., Barbic, J.: A deep emulator for secondary motion of 3D characters. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 5932\u20135940 (2021)","DOI":"10.1109\/CVPR46437.2021.00587"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20068-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T08:11:43Z","timestamp":1668067903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20068-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200670","9783031200687"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20068-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}