{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:23:42Z","timestamp":1780392222594,"version":"3.54.1"},"publisher-location":"Cham","reference-count":63,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200649","type":"print"},{"value":"9783031200656","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\/978-3-031-20065-6_28","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:24:03Z","timestamp":1667420643000},"page":"479-496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["D &D: Learning Human Dynamics from Dynamic Camera"],"prefix":"10.1007","author":[{"given":"Jiefeng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Yu","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":[[2022,11,3]]},"reference":[{"key":"28_CR1","unstructured":"Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., Kolter, J.Z.: Differentiable convex optimization layers. In: NeurIPS (2019)"},{"key":"28_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":"28_CR3","doi-asserted-by":"crossref","unstructured":"Andrews, S., Huerta, I., Komura, T., Sigal, L., Mitchell, K.: Real-time physics-based motion capture with sparse sensors. In: CVMP (2016)","DOI":"10.1145\/2998559.2998564"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Arnab, A., Doersch, C., Zisserman, A.: Exploiting temporal context for 3D human pose estimation in the wild. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00351"},{"key":"28_CR5","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":"28_CR6","doi-asserted-by":"crossref","unstructured":"Choi, H., Moon, G., Chang, J.Y., Lee, K.M.: Beyond static features for temporally consistent 3D human pose and shape from a video. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00200"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Dabral, R., Shimada, S., Jain, A., Theobalt, C., Golyanik, V.: Gravity-aware monocular 3D human-object reconstruction. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01214"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Fang, H., Xu, Y., Wang, W., Liu, X., Zhu, S.C.: Learning pose grammar to encode human body configuration for 3D pose estimation. In: AAAI (2017)","DOI":"10.1609\/aaai.v32i1.12270"},{"key":"28_CR9","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. In: TPAMI (2013)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"28_CR10","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-Softmax. In: ICLR (2017)"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Joo, H., Neverova, N., Vedaldi, A.: Exemplar fine-tuning for 3D human model fitting towards in-the-wild 3D human pose estimation. In: 3DV (2021)","DOI":"10.1109\/3DV53792.2021.00015"},{"key":"28_CR12","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":"28_CR13","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3D human dynamics from video. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00576"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Athanasiou, N., Black, M.J.: Vibe: video inference for human body pose and shape estimation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"28_CR15","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":"28_CR16","doi-asserted-by":"crossref","unstructured":"Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M.J., Gehler, P.V.: Unite the people: closing the loop between 3D and 2D human representations. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.500"},{"key":"28_CR17","unstructured":"Levine, S., Popovi\u0107, J.: Physically plausible simulation for character animation. In: SIGGRAPH (2012)"},{"key":"28_CR18","first-page":"27236","volume":"34","author":"J Li","year":"2021","unstructured":"Li, J., et al.: Localization with sampling-argmax. Adv. Neural. Inf. Process. Syst. 34, 27236\u201327248 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, C., Chen, Z., Bian, S., Yang, L., Lu, C.: Hybrik: a hybrid analytical-neural inverse kinematics solution for 3D human pose and shape estimation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00339"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y.L., et al.: Detailed 2D\u20133D joint representation for human-object interaction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10166\u201310175 (2020)","DOI":"10.1109\/CVPR42600.2020.01018"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y.L., et al.: Pastanet: toward human activity knowledge engine. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 382\u2013391 (2020)","DOI":"10.1109\/CVPR42600.2020.00046"},{"key":"28_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. In: TOG (2015)","DOI":"10.1145\/2816795.2818013"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Luo, Z., Golestaneh, S.A., Kitani, K.M.: 3D human motion estimation via motion compression and refinement. In: ACCV (2020)","DOI":"10.1007\/978-3-030-69541-5_20"},{"key":"28_CR24","unstructured":"Luo, Z., Hachiuma, R., Yuan, Y., Kitani, K.: Dynamics-regulated kinematic policy for egocentric pose estimation. In: NeurIPS (2021)"},{"key":"28_CR25","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: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00554"},{"key":"28_CR26","doi-asserted-by":"crossref","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: ECCV (2018)","DOI":"10.1007\/978-3-030-01249-6_37"},{"key":"28_CR27","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":"28_CR28","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":"28_CR29","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: XNect: real-time multi-person 3D motion capture with a single RGB camera. TOG (2020)","DOI":"10.1145\/3386569.3392410"},{"key":"28_CR30","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"},{"issue":"4","key":"28_CR31","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. TOG 36(4), 1\u201314 (2017)","journal-title":"TOG"},{"key":"28_CR32","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":"28_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1007\/978-3-030-58571-6_44","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Moon","year":"2020","unstructured":"Moon, G., Lee, K.M.: I2L-MeshNet: Image-to-lixel prediction network for accurate 3D human pose and mesh estimation from a single RGB image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 752\u2013768. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58571-6_44"},{"key":"28_CR34","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":"28_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/978-3-319-49409-8_15","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"S Park","year":"2016","unstructured":"Park, S., Hwang, J., Kwak, N.: 3D human pose estimation using convolutional neural networks with 2D pose information. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 156\u2013169. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_15"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01123"},{"key":"28_CR37","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":"28_CR38","unstructured":"Peng, X.B., Chang, M., Zhang, G., Abbeel, P., Levine, S.: MCP: learning composable hierarchical control with multiplicative compositional policies. arXiv preprint arXiv:1905.09808 (2019)"},{"key":"28_CR39","doi-asserted-by":"crossref","unstructured":"Rempe, D., Birdal, T., Hertzmann, A., Yang, J., Sridhar, S., Guibas, L.J.: Humor: 3D human motion model for robust pose estimation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01129"},{"key":"28_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-030-58558-7_5","volume-title":"Computer Vision \u2013 ECCV 2020","author":"D Rempe","year":"2020","unstructured":"Rempe, D., Guibas, L.J., Hertzmann, A., Russell, B., Villegas, R., Yang, J.: Contact and human dynamics from monocular video. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 71\u201387. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_5"},{"key":"28_CR41","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":"28_CR42","doi-asserted-by":"crossref","unstructured":"Shahabpoor, E., Pavic, A.: Measurement of walking ground reactions in real-life environments: a systematic review of techniques and technologies. Sensors (2017)","DOI":"10.3390\/s17092085"},{"key":"28_CR43","doi-asserted-by":"crossref","unstructured":"Shimada, S., Golyanik, V., Xu, W., P\u00e9rez, P., Theobalt, C.: Neural monocular 3D human motion capture with physical awareness. TOG (2021)","DOI":"10.1145\/3476576.3476640"},{"key":"28_CR44","doi-asserted-by":"crossref","unstructured":"Shimada, S., Golyanik, V., Xu, W., Theobalt, C.: Physcap: physically plausible monocular 3D motion capture in real time. TOG (2020)","DOI":"10.1145\/3414685.3417877"},{"key":"28_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1007\/978-3-030-58565-5_44","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Song","year":"2020","unstructured":"Song, J., Chen, X., Hilliges, O.: Human body model fitting by learned gradient descent. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 744\u2013760. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_44"},{"key":"28_CR46","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":"28_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1007\/978-3-030-01231-1_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Sun","year":"2018","unstructured":"Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 536\u2013553. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_33"},{"key":"28_CR48","doi-asserted-by":"crossref","unstructured":"Sun, Y., Ye, Y., Liu, W., Gao, W., Fu, Y., Mei, T.: Human mesh recovery from monocular images via a skeleton-disentangled representation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00545"},{"key":"28_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-01234-2_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Varol","year":"2018","unstructured":"Varol, G., Ceylan, D., Russell, B., Yang, J., Yumer, E., Laptev, I., Schmid, C.: BodyNet: volumetric inference of 3D human body shapes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 20\u201338. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_2"},{"key":"28_CR50","doi-asserted-by":"crossref","unstructured":"Wan, Z., Li, Z., Tian, M., Liu, J., Yi, S., Li, H.: Encoder-decoder with multi-level attention for 3D human shape and pose estimation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01279"},{"key":"28_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1007\/978-3-030-58580-8_15","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Wang","year":"2020","unstructured":"Wang, C., Li, J., Liu, W., Qian, C., Lu, C.: HMOR: hierarchical multi-person ordinal relations for monocular multi-person 3D pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 242\u2013259. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_15"},{"key":"28_CR52","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: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00622"},{"issue":"4","key":"28_CR53","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/j.mechmachtheory.2009.12.001","volume":"45","author":"C Yang","year":"2010","unstructured":"Yang, C., Huang, Q., Jiang, H., Peter, O.O., Han, J.: PD control with gravity compensation for hydraulic 6-DOF parallel manipulator. Mech. Mach. Theory 45(4), 666\u2013677 (2010)","journal-title":"Mech. Mach. Theory"},{"key":"28_CR54","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":"28_CR55","doi-asserted-by":"crossref","unstructured":"Yu, R., Park, H., Lee, J.: Human dynamics from monocular video with dynamic camera movements. TOG (2021)","DOI":"10.1145\/3478513.3480504"},{"key":"28_CR56","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Iqbal, U., Molchanov, P., Kitani, K., Kautz, J.: Glamr: global occlusion-aware human mesh recovery with dynamic cameras. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01076"},{"key":"28_CR57","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Kitani, K.: Ego-pose estimation and forecasting as real-time PD control. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01018"},{"key":"28_CR58","unstructured":"Yuan, Y., Kitani, K.: Residual force control for agile human behavior imitation and extended motion synthesis. In: NeurIPS (2020)"},{"key":"28_CR59","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Wei, S.E., Simon, T., Kitani, K., Saragih, J.: Simpoe: simulated character control for 3D human pose estimation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00708"},{"key":"28_CR60","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/978-3-030-58574-7_5","volume-title":"Computer Vision \u2013 ECCV 2020","author":"P Zell","year":"2020","unstructured":"Zell, P., Rosenhahn, B., Wandt, B.: Weakly-supervised learning of human dynamics. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 68\u201384. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58574-7_5"},{"key":"28_CR61","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-58568-6_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Zeng","year":"2020","unstructured":"Zeng, A., Sun, X., Huang, F., Liu, M., Xu, Q., Lin, S.: SRNet: improving generalization in 3D human pose estimation with a split-and-recombine approach. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 507\u2013523. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_30"},{"key":"28_CR62","doi-asserted-by":"crossref","unstructured":"Zeng, A., Yang, L., Ju, X., Li, J., Wang, J., Xu, Q.: Smoothnet: a plug-and-play network for refining human poses in videos. arXiv preprint arXiv:2112.13715 (2021)","DOI":"10.1007\/978-3-031-20065-6_36"},{"key":"28_CR63","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"}],"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\/978-3-031-20065-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T03:37:43Z","timestamp":1728272263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20065-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200649","9783031200656"],"references-count":63,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20065-6_28","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":"3 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)"}}]}}