{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:23:15Z","timestamp":1780392195544,"version":"3.54.1"},"publisher-location":"Cham","reference-count":73,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585792","type":"print"},{"value":"9783030585808","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\/978-3-030-58580-8_15","type":"book-chapter","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T07:03:09Z","timestamp":1606892589000},"page":"242-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["HMOR: Hierarchical Multi-person Ordinal Relations for Monocular Multi-person 3D Pose Estimation"],"prefix":"10.1007","author":[{"given":"Can","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiefeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cewu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Aitpayev, K., Gaber, J.: Creation of 3D human avatar using kinect. ATFECM 01, 1\u20133 (2012)","DOI":"10.1109\/ICAICT.2012.6398480"},{"key":"15_CR2","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":"15_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":"15_CR4","doi-asserted-by":"crossref","unstructured":"Boulic, R., B\u00e9cheiraz, P., Emering, L., Thalmann, D.: Integration of motion control techniques for virtual human and avatar real-time animation. In: VRST (1997)","DOI":"10.1145\/261135.261156"},{"key":"15_CR5","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"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: Human pose estimation with iterative error feedback. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.512"},{"key":"15_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":"15_CR8","unstructured":"Chen, W., Fu, Z., Yang, D., Deng, J.: Single-image depth perception in the wild. In: NeurIPS (2016)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Chen, X., Lin, K.Y., Liu, W., Qian, C., Lin, L.: Weakly-supervised discovery of geometry-aware representation for 3D human pose estimation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01115"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00742"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR (2010)","DOI":"10.1109\/CVPR.2010.5540094"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Du, G., Zhang, P.: Markerless human-robot interface for dual robot manipulators using kinect sensor. Robot Comput. Int. Manuf. 30(2), 150\u2013159 (2014)","DOI":"10.1016\/j.rcim.2013.09.003"},{"key":"15_CR14","unstructured":"Dumoulin, V., Visin, F.: A guide to convolution arithmetic for deep learning (2016). arXiv:1603.07285"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Cao, J., Tai, Y.W., Lu, C.: Pairwise body-part attention for recognizing human-object interactions. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01249-6_4"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Xie, S., Tai, Y.W., Lu, C.: RMPE: Regional multi-person pose estimation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.256"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Xu, Y., Wang, W., Liu, X., Zhu, S.C.: Learning pose grammar to encode human body configuration for 3D pose estimation. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12270"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"15_CR19","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":"15_CR20","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6m: large scale datasets and predictive methods for 3D human sensing in natural environments. TPAMI 36, 1325\u20131339 (2014)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Jin, S., Liu, W., Ouyang, W., Qian, C.: Multi-person articulated tracking with spatial and temporal embeddings. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00581"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Joo, H., et al.: Panoptic studio: a massively multiview system for social motion capture. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.381"},{"key":"15_CR24","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: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00744"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Karagoz, S., Akbas, E.: MultiPoseNet: fast multi-person pose estimation using pose residual network. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01252-6_26"},{"key":"15_CR26","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: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00234"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, C., Zhu, H., Mao, Y., Fang, H.S., Lu, C.: Crowdpose: efficient crowded scenes pose estimation and a new benchmark. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01112"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y.L., et al.: Detailed 2d\u20133d joint representation for human-object interaction. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01018"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Li, Y.L., et al.: Pastanet: toward human activity knowledge engine. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00046"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Luvizon, D.C., Tabia, H., Picard, D.: Learning features combination for human action recognition from skeleton sequences. Pattern Recogn. Lett. 99, 13\u201320 (2017)","DOI":"10.1016\/j.patrec.2017.02.001"},{"key":"15_CR33","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: ECCV (2018)","DOI":"10.1007\/978-3-030-01249-6_37"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Marsaglia, G., et al.: Choosing a point from the surface of a sphere. Ann. Math. Stat. 43, 645\u2013646 (1972)","DOI":"10.1214\/aoms\/1177692644"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Martinez, J., Hossain, R., Romero, J., Little, J.J.: A simple yet effective baseline for 3D human pose estimation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"15_CR36","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":"15_CR37","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Xnect: Real-time multi-person 3D human pose estimation with a single RGB camera (2019). arXiv preprint arXiv:1907.00837","DOI":"10.1145\/3386569.3392410"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Single-shot multi-person 3D pose estimation from monocular RGB. In: 3DV (2018)","DOI":"10.1109\/3DV.2018.00024"},{"key":"15_CR39","doi-asserted-by":"crossref","unstructured":"Moon, G., Chang, J.Y., Lee, K.M.: Camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01023"},{"key":"15_CR40","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":"15_CR41","doi-asserted-by":"crossref","unstructured":"Narihira, T., Maire, M., Yu, S.X.: Learning lightness from human judgement on relative reflectance. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298915"},{"key":"15_CR42","unstructured":"Newell, A., Huang, Z., Deng, J.: Associative embedding: End-to-end learning for joint detection and grouping. In: NeurIPS (2017)"},{"key":"15_CR43","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"15_CR44","doi-asserted-by":"crossref","unstructured":"Pang, B., Zha, K., Cao, H., Shi, C., Lu, C.: Deep RNN framework for visual sequential applications. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00051"},{"key":"15_CR45","doi-asserted-by":"crossref","unstructured":"Park, S., Hwang, J., Kwak, N.: 3D human pose estimation using convolutional neural networks with 2D pose information. In: ECCV (2016)","DOI":"10.1007\/978-3-319-49409-8_15"},{"key":"15_CR46","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Daniilidis, K.: Ordinal depth supervision for 3D human pose estimation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00763"},{"key":"15_CR47","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":"15_CR48","unstructured":"Pirinen, A., G\u00e4rtner, E., Sminchisescu, C.: Domes to drones: self-supervised active triangulation for 3D human pose reconstruction. In: NeurIPS (2019)"},{"key":"15_CR49","doi-asserted-by":"crossref","unstructured":"Popa, A.I., Zanfir, M., Sminchisescu, C.: Deep multitask architecture for integrated 2D and 3D human sensing. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.501"},{"key":"15_CR50","doi-asserted-by":"crossref","unstructured":"Presti, L.L., La Cascia, M.: 3D skeleton-based human action classification: a survey. Pattern Recogn. 53, 130\u2013147 (2016)","DOI":"10.1016\/j.patcog.2015.11.019"},{"key":"15_CR51","doi-asserted-by":"crossref","unstructured":"Qi, S., Wang, W., Jia, B., Shen, J., Zhu, S.C.: Learning human-object interactions by graph parsing neural networks. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01240-3_25"},{"key":"15_CR52","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":"15_CR53","doi-asserted-by":"crossref","unstructured":"Rogez, G., Weinzaepfel, P., Schmid, C.: LCR-Net++: multi-person 2D and 3D pose detection in natural images. TPAMI (2019)","DOI":"10.1109\/TPAMI.2019.2892985"},{"key":"15_CR54","unstructured":"Ronchi, M.R., Mac Aodha, O., Eng, R., Perona, P.: It\u2019s all relative: monocular 3D human pose estimation from weakly supervised data. In: BMVC (2018)"},{"key":"15_CR55","doi-asserted-by":"crossref","unstructured":"Sharma, S., Varigonda, P.T., Bindal, P., Sharma, A., Jain, A.: Monocular 3D human pose estimation by generation and ordinal ranking. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00241"},{"key":"15_CR56","doi-asserted-by":"crossref","unstructured":"Sigal, L., Balan, A.O., Black, M.J.: Humaneva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. IJCV 87, 1\u201324 (2010)","DOI":"10.1007\/s11263-009-0273-6"},{"key":"15_CR57","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556"},{"key":"15_CR58","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"15_CR59","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":"15_CR60","doi-asserted-by":"crossref","unstructured":"Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01231-1_33"},{"key":"15_CR61","doi-asserted-by":"crossref","unstructured":"Wang, M., Chen, X., Liu, W., Qian, C., Lin, L., Ma, L.: Drpose3d: depth ranking in 3D human pose estimation. IJCAI (2018)","DOI":"10.24963\/ijcai.2018\/136"},{"key":"15_CR62","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"15_CR63","unstructured":"Xiu, Y., Li, J., Wang, H., Fang, Y., Lu, C.: Pose flow: efficient online pose tracking (2018). arXiv preprint arXiv:1802.00977"},{"key":"15_CR64","doi-asserted-by":"crossref","unstructured":"Yang, W., Ouyang, W., Wang, X., Ren, J., Li, H., Wang, X.: 3D human pose estimation in the wild by adversarial learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00551"},{"key":"15_CR65","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":"15_CR66","doi-asserted-by":"crossref","unstructured":"Zanfir, A., Marinoiu, E., Sminchisescu, C.: Monocular 3D pose and shape estimation of multiple people in natural scenes-the importance of multiple scene constraints. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00229"},{"key":"15_CR67","unstructured":"Zanfir, A., Marinoiu, E., Zanfir, M., Popa, A.I., Sminchisescu, C.: Deep network for the integrated 3D sensing of multiple people in natural images. In: NeurIPS (2018)"},{"key":"15_CR68","doi-asserted-by":"crossref","unstructured":"Zhou, K., Han, X., Jiang, N., Jia, K., Lu, J.: Hemlets pose: learning part-centric heatmap triplets for accurate 3d human pose estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00243"},{"key":"15_CR69","doi-asserted-by":"crossref","unstructured":"Zhou, T., Krahenbuhl, P., Efros, A.A.: Learning data-driven reflectance priors for intrinsic image decomposition. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.396"},{"key":"15_CR70","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhu, M., Pavlakos, G., Leonardos, S., Derpanis, K.G., Daniilidis, K.: Monocap: monocular human motion capture using a CNN coupled with a geometric prior. TPAMI (2018)","DOI":"10.1109\/TPAMI.2018.2816031"},{"key":"15_CR71","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":"15_CR72","doi-asserted-by":"crossref","unstructured":"Zimmermann, C., Welschehold, T., Dornhege, C., Burgard, W., Brox, T.: 3D human pose estimation in RGBD images for robotic task learning. In: ICRA (2018)","DOI":"10.1109\/ICRA.2018.8462833"},{"key":"15_CR73","doi-asserted-by":"crossref","unstructured":"Zoran, D., Isola, P., Krishnan, D., Freeman, W.T.: Learning ordinal relationships for mid-level vision. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.52"}],"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\/978-3-030-58580-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:07:02Z","timestamp":1733098022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58580-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585792","9783030585808"],"references-count":73,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58580-8_15","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":"3 December 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. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","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)"}}]}}