{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T12:52:57Z","timestamp":1780923177058,"version":"3.54.1"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030695408","type":"print"},{"value":"9783030695415","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-3-030-69541-5_20","type":"book-chapter","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T11:03:47Z","timestamp":1614251027000},"page":"324-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["3D Human Motion Estimation via Motion Compression and Refinement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1842-7622","authenticated-orcid":false,"given":"Zhengyi","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3230-2797","authenticated-orcid":false,"given":"S. Alireza","family":"Golestaneh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9389-4060","authenticated-orcid":false,"given":"Kris M.","family":"Kitani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Athanasiou, N., Black, M.J.: VIBE: video inference for human body pose and shape estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5253\u20135263 (2020)","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhu, S.C., Tung, T.: DenseRaC: joint 3D pose and shape estimation by dense render-and-compare. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7759\u20137769 (2019)","DOI":"10.1109\/ICCV.2019.00785"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2252\u20132261 (2019)","DOI":"10.1109\/ICCV.2019.00234"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Georgakis, G.V., Li, R., Karanam, S., Chen, T., Kosecka, J., Wu, Z.: Hierarchical kinematic human mesh recovery. ArXiv abs\/2003.04232 (2020)","DOI":"10.1007\/978-3-030-58520-4_45"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"G\u00fcler, R.A., Neverova, N., Kokkinos, I.: DensePose: dense human pose estimation in the wild. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7297\u20137306 (2018)","DOI":"10.1109\/CVPR.2018.00762"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3D human dynamics from video. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5607\u20135616 (2019)","DOI":"10.1109\/CVPR.2019.00576"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7122\u20137131 (2018)","DOI":"10.1109\/CVPR.2018.00744"},{"key":"20_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-030-01249-6_5","volume-title":"Computer Vision \u2013 ECCV 2018","author":"MRI Hossain","year":"2018","unstructured":"Hossain, M.R.I., Little, J.J.: Exploiting temporal information for 3D human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 69\u201386. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_5"},{"key":"20_CR9","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: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 5441\u20135450 (2019)","DOI":"10.1109\/ICCV.2019.00554"},{"key":"20_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-030-01249-6_37","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T von Marcard","year":"2018","unstructured":"von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3D human pose in the wild using IMUs and a moving camera. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 614\u2013631. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_37"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10967\u201310977 (2019)","DOI":"10.1109\/CVPR.2019.01123"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"248:1","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, 248:1\u2013248:16 (2015)","journal-title":"ACM Trans. Graph."},{"key":"20_CR13","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, 408\u2013416 (2005)","journal-title":"ACM Trans. Graph."},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Grauman, K., Shakhnarovich, G., Darrell, T.: Inferring 3D structure with a statistical image-based shape model. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 1, pp. 641\u2013648 (2003)","DOI":"10.1109\/ICCV.2003.1238408"},{"key":"20_CR15","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/TPAMI.2006.21","volume":"28","author":"A Agarwal","year":"2006","unstructured":"Agarwal, A., Triggs, B.: Recovering 3D human pose from monocular images. IEEE Trans. Pattern Anal. Mach. Intell. 28, 44\u201358 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR16","unstructured":"Sigal, L., Balan, A., Black, M.J.: Combined discriminative and generative articulated pose and non-rigid shape estimation. In: Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference, pp. 1\u20138 (2009)"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Zhou, S., Fu, H., Liu, L., Cohen-Or, D., Han, X.: Parametric reshaping of human bodies in images. In: ACM SIGGRAPH 2010 Papers, SIGGRAPH 2010, vol. 29, pp. 1\u201310 (2010)","DOI":"10.1145\/1833349.1778863"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Peng Guan, Weiss, A., B\u00e3lan, A.O., Black, M.J.: Estimating human shape and pose from a single image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1381\u20131388 (2009)","DOI":"10.1109\/ICCV.2009.5459300"},{"key":"20_CR19","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":"20_CR20","doi-asserted-by":"crossref","unstructured":"Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., Schiele, B.: Neural body fitting: unifying deep learning and model based human pose and shape estimation. In: Proceedings - 2018 International Conference on 3D Vision, 3DV 2018, pp. 484\u2013494 (2018)","DOI":"10.1109\/3DV.2018.00062"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Guler, R.A., Kokkinos, I.: HoloPose: holistic 3D human reconstruction in-the-wild. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2019, pp. 10876\u201310886 (2019)","DOI":"10.1109\/CVPR.2019.01114"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Tan, J.K.V., Budvytis, I., Cipolla, R.: Indirect deep structured learning for 3D human body shape and pose prediction. In: British Machine Vision Conference 2017, BMVC 2017, pp. 1\u201311 (2017)","DOI":"10.5244\/C.31.15"},{"key":"20_CR23","unstructured":"Tung, H.Y.F., Tung, H.W., Yumer, E., Fragkiadaki, K.: Self-supervised learning of motion capture. In: Advances in Neural Information Processing Systems, December 2017, pp. 5237\u20135247 (2017)"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhu, L., Zhou, X., Daniilidis, K.: Learning to estimate 3D human pose and shape from a single color image. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 459\u2013468 (2018)","DOI":"10.1109\/CVPR.2018.00055"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3D human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2019, pp. 7745\u20137754 (2019)","DOI":"10.1109\/CVPR.2019.00794"},{"key":"20_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/978-3-030-01240-3_41","volume-title":"Computer Vision \u2013 ECCV 2018","author":"R Dabral","year":"2018","unstructured":"Dabral, R., Mundhada, A., Kusupati, U., Afaque, S., Sharma, A., Jain, A.: Learning 3D human pose from structure and motion. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 679\u2013696. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_41"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Xu, J., Yu, Z., Ni, B., Yang, J., Yang, X., Zhang, W.: Deep kinematics analysis for monocular 3D human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00098"},{"key":"20_CR28","doi-asserted-by":"publisher","first-page":"1","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. 36, 1\u201313 (2017)","journal-title":"ACM Trans. Graph."},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Xnect: real-time multi-person 3D motion capture with a single RGB camera. In: SIGGRAPH 2020 (2020)","DOI":"10.1145\/3386569.3392410"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Ren, L., Patrick, A., Efros, A.A., Hodgins, J.K., Rehg, J.M.: A data-driven approach to quantifying natural human motion. In: SIGGRAPH 2005 (2005)","DOI":"10.1145\/1186822.1073316"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Urtasun, R., Fleet, D.J., Fua, P.: 3D people tracking with Gaussian process dynamical models. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 238\u2013245 (2006)","DOI":"10.1109\/CVPR.2006.15"},{"key":"20_CR32","unstructured":"Ormoneit, D., Sidenbladh, H., Black, M.J., Hastie, T.: Learning and tracking cyclic human motion. In: Advances in Neural Information Processing Systems (2001)"},{"key":"20_CR33","unstructured":"Wang, Z., et al.: Learning diverse stochastic human-action generators by learning smooth latent transitions. ArXiv abs\/1912.10150 (2020)"},{"key":"20_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/978-3-030-01216-8_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Cai","year":"2018","unstructured":"Cai, H., Bai, C., Tai, Y.-W., Tang, C.-K.: Deep video generation, prediction and completion of human action sequences. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 374\u2013390. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_23"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Walker, J., Marino, K., Gupta, A., Hebert, M.: The pose knows: video forecasting by generating pose futures. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.361"},{"key":"20_CR36","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.robot.2018.07.006","volume":"109","author":"M Plappert","year":"2018","unstructured":"Plappert, M., Mandery, C., Asfour, T.: Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks. Robot. Auton. Syst. 109, 13\u201326 (2018)","journal-title":"Robot. Auton. Syst."},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Ahuja, C., Morency, L.P.: Language2pose: Natural language grounded pose forecasting. In: 2019 International Conference on 3D Vision (3DV), pp. 719\u2013728 (2019)","DOI":"10.1109\/3DV.2019.00084"},{"key":"20_CR38","doi-asserted-by":"publisher","first-page":"3441","DOI":"10.1109\/LRA.2018.2852838","volume":"3","author":"T Yamada","year":"2018","unstructured":"Yamada, T., Matsunaga, H., Ogata, T.: Paired recurrent autoencoders for bidirectional translation between robot actions and linguistic descriptions. IEEE Robot. Autom. Lett. 3, 3441\u20133448 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"20_CR39","unstructured":"Lee, H.Y., et al.: Dancing to music. In: NeurIPS (2019)"},{"key":"20_CR40","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision 2015 Inter, pp. 4346\u20134354 (2015)","DOI":"10.1109\/ICCV.2015.494"},{"key":"20_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-030-01228-1_17","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Yan","year":"2018","unstructured":"Yan, X., et al.: MT-VAE: learning motion transformations to generate multimodal human dynamics. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 276\u2013293. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_17"},{"key":"20_CR42","unstructured":"Yuan, Y., Kitani, K.: Diverse trajectory forecasting with determinantal point processes. In: International Conference on Learning Representations (2020)"},{"key":"20_CR43","doi-asserted-by":"crossref","unstructured":"Butepage, J., Black, M.J., Kragic, D., Kjellstrom, H.: Deep representation learning for human motion prediction and classification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.173"},{"key":"20_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-030-58545-7_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Yuan","year":"2020","unstructured":"Yuan, Y., Kitani, K.: DLow: diversifying latent flows for diverse human motion prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 346\u2013364. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_20"},{"key":"20_CR45","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5738\u20135746 (2019)","DOI":"10.1109\/CVPR.2019.00589"},{"key":"20_CR46","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, pp. 1\u201314 (2014)"},{"key":"20_CR47","doi-asserted-by":"crossref","unstructured":"Walker, J., Marino, K., Gupta, A., Hebert, M.: The pose knows: video forecasting by generating pose futures. In: Proceedings of the IEEE International Conference on Computer Vision, October 2017, pp. 3352\u20133361 (2017)","DOI":"10.1109\/ICCV.2017.361"},{"key":"20_CR48","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Monocular 3D human pose estimation in the wild using improved CNN supervision. In: 2017 International Conference on 3D Vision (3DV), pp. 506\u2013516 (2017)","DOI":"10.1109\/3DV.2017.00064"},{"key":"20_CR49","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, 1325\u20131339 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhu, M., Derpanis, K.G.: From actemes to action: a strongly-supervised representation for detailed action understanding. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2248\u20132255 (2013)","DOI":"10.1109\/ICCV.2013.280"},{"key":"20_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2661229.2661273","volume":"33","author":"M Loper","year":"2014","unstructured":"Loper, M., Mahmoody, N., Blackz, M.J.: MoSh: motion and shape capture from sparse markers. ACM Trans. Graph. 33, 1\u201313 (2014)","journal-title":"ACM Trans. Graph."},{"key":"20_CR52","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.143"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69541-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T21:10:20Z","timestamp":1724533820000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69541-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695408","9783030695415"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69541-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","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":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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":"3","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.","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)"}}]}}