{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:00:17Z","timestamp":1765357217432,"version":"3.40.3"},"publisher-location":"Cham","reference-count":64,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200465"},{"type":"electronic","value":"9783031200472"}],"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\/978-3-031-20047-2_30","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T10:02:55Z","timestamp":1666432975000},"page":"516-533","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["HULC: 3D HUman Motion Capture with\u00a0Pose Manifold SampLing and\u00a0Dense Contact Guidance"],"prefix":"10.1007","author":[{"given":"Soshi","family":"Shimada","sequence":"first","affiliation":[]},{"given":"Vladislav","family":"Golyanik","sequence":"additional","affiliation":[]},{"given":"Zhi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"P\u00e9rez","sequence":"additional","affiliation":[]},{"given":"Weipeng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Theobalt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"30_CR1","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":"30_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/978-3-030-58452-8_23","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Cao","year":"2020","unstructured":"Cao, Z., Gao, H., Mangalam, K., Cai, Q.-Z., Vo, M., Malik, J.: Long-term human motion prediction with scene context. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 387\u2013404. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_23"},{"unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: realtime multi-person 2d pose estimation using part affinity fields. Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2019)","key":"30_CR3"},{"key":"30_CR4","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/s11263-013-0672-6","volume":"110","author":"J Charles","year":"2013","unstructured":"Charles, J., Pfister, T., Everingham, M., Zisserman, A.: Automatic and efficient human pose estimation for sign language videos. Int. J. Comput. Vision 110, 70\u201390 (2013)","journal-title":"Int. J. Comput. Vision"},{"doi-asserted-by":"crossref","unstructured":"Chen, C., Ramanan, D.: 3d human pose estimation = 2d pose estimation + matching. In: Computer Vision and Pattern Recognition (CVPR) (2017)","key":"30_CR5","DOI":"10.1109\/CVPR.2017.610"},{"doi-asserted-by":"crossref","unstructured":"Choi, H., Moon, G., Lee, K.M.: Beyond static features for temporally consistent 3d human pose and shape from a video. In: Computer Vision and Pattern Recognition (CVPR) (2021)","key":"30_CR6","DOI":"10.1109\/CVPR46437.2021.00200"},{"doi-asserted-by":"crossref","unstructured":"Dabral, R., Shimada, S., Jain, A., Theobalt, C., Golyanik, V.: Gravity-aware monocular 3d human-object reconstruction. In: International Conference on Computer Vision (ICCV) (2021)","key":"30_CR7","DOI":"10.1109\/ICCV48922.2021.01214"},{"doi-asserted-by":"crossref","unstructured":"Fieraru, M., Zanfir, M., Oneata, E., Popa, A.I., Olaru, V., Sminchisescu, C.: Three-dimensional reconstruction of human interactions. In: Computer Vision and Pattern Recognition (CVPR) (2020)","key":"30_CR8","DOI":"10.1109\/CVPR42600.2020.00724"},{"doi-asserted-by":"crossref","unstructured":"Habibie, I., Xu, W., Mehta, D., Pons-Moll, G., Theobalt, C.: In the wild human pose estimation using explicit 2d features and intermediate 3d representations. In: Computer Vision and Pattern Recognition (CVPR) (2019)","key":"30_CR9","DOI":"10.1109\/CVPR.2019.01116"},{"doi-asserted-by":"crossref","unstructured":"Hassan, M., Ceylan, D., Villegas, R., Saito, J., Yang, J., Zhou, Y., Black, M.J.: Stochastic scene-aware motion prediction. In: International Conference on Computer Vision (ICCV) (2021)","key":"30_CR10","DOI":"10.1109\/ICCV48922.2021.01118"},{"doi-asserted-by":"crossref","unstructured":"Hassan, M., Choutas, V., Tzionas, D., Black, M.J.: Resolving 3D human pose ambiguities with 3D scene constraints. In: International Conference on Computer Vision (ICCV) (2019)","key":"30_CR11","DOI":"10.1109\/ICCV.2019.00237"},{"doi-asserted-by":"crossref","unstructured":"Hassan, M., Ghosh, P., Tesch, J., Tzionas, D., Black, M.J.: Populating 3D scenes by learning human-scene interaction. In: Computer Vision and Pattern Recognition (CVPR) (2021)","key":"30_CR12","DOI":"10.1109\/CVPR46437.2021.01447"},{"doi-asserted-by":"crossref","unstructured":"Jiang, W., Kolotouros, N., Pavlakos, G., Zhou, X., Daniilidis, K.: Coherent reconstruction of multiple humans from a single image. In: Computer Vision and Pattern Recognition (CVPR) (2020)","key":"30_CR13","DOI":"10.1109\/CVPR42600.2020.00562"},{"unstructured":"John, V., Trucco, E., McKenna, S.: Markerless human motion capture using charting and manifold constrained particle swarm optimisation. In: British Machine Vision Conference (BMVC) (2010)","key":"30_CR14"},{"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: Computer Vision and Pattern Recognition (CVPR) (2018)","key":"30_CR15","DOI":"10.1109\/CVPR.2018.00744"},{"doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3d human dynamics from video. In: Computer Vision and Pattern Recognition (CVPR) (2019)","key":"30_CR16","DOI":"10.1109\/CVPR.2019.00576"},{"unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (ICLR) (2014)","key":"30_CR17"},{"doi-asserted-by":"crossref","unstructured":"Knauer, C., L\u00f6ffler, M., Scherfenberg, M., Wolle, T.: The directed hausdorff distance between imprecise point sets. In: International Symposium on Algorithms and Computation (ISAAC) (2009)","key":"30_CR18","DOI":"10.1007\/978-3-642-10631-6_73"},{"doi-asserted-by":"crossref","unstructured":"Kocabas, M., Athanasiou, N., Black, M.J.: Vibe: video inference for human body pose and shape estimation. In: Computer Vision and Pattern Recognition (CVPR) (2020)","key":"30_CR19","DOI":"10.1109\/CVPR42600.2020.00530"},{"doi-asserted-by":"crossref","unstructured":"Kocabas, M., Huang, C.H.P., Hilliges, O., Black, M.J.: PARE: part attention regressor for 3D human body estimation. In: International Conference on Computer Vision (ICCV) (2021)","key":"30_CR20","DOI":"10.1109\/ICCV48922.2021.01094"},{"doi-asserted-by":"crossref","unstructured":"Kocabas, M., Huang, C.H.P., Tesch, J., M\u00fcller, L., Hilliges, O., Black, M.J.: SPEC: seeing people in the wild with an estimated camera. In: International Conference on Computer Vision (ICCV) (2021)","key":"30_CR21","DOI":"10.1109\/ICCV48922.2021.01085"},{"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: International Conference on Computer Vision (ICCV) (2019)","key":"30_CR22","DOI":"10.1109\/ICCV.2019.00234"},{"doi-asserted-by":"crossref","unstructured":"Kolotouros, N., Pavlakos, G., Jayaraman, D., Daniilidis, K.: Probabilistic modeling for human mesh recovery. In: International Conference on Computer Vision (ICCV) (2021)","key":"30_CR23","DOI":"10.1109\/ICCV48922.2021.01140"},{"doi-asserted-by":"crossref","unstructured":"Li, Z., Shimada, S., Schiele, B., Theobalt, C., Golyanik, V.: Mocapdeform: monocular 3d human motion capture in deformable scenes. In: Arxiv (2022)","key":"30_CR24","DOI":"10.1145\/3476576.3476640"},{"doi-asserted-by":"crossref","unstructured":"Li, Z., Sedlar, J., Carpentier, J., Laptev, I., Mansard, N., Sivic, J.: Estimating 3d motion and forces of person-object interactions from monocular video. In: Computer Vision and Pattern Recognition (CVPR) (2019)","key":"30_CR25","DOI":"10.1109\/CVPR.2019.00884"},{"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: International Conference on Computer Vision (ICCV) (2019)","key":"30_CR26","DOI":"10.1109\/ICCV.2019.00554"},{"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: International Conference on Computer Vision (ICCV) (2017)","key":"30_CR27","DOI":"10.1109\/ICCV.2017.288"},{"doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Monocular 3d human pose estimation in the wild using improved CNN supervision. In: International Conference on 3D Vision (3DV) (2017)","key":"30_CR28","DOI":"10.1109\/3DV.2017.00064"},{"issue":"4","key":"30_CR29","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1145\/3386569.3392410","volume":"39","author":"D Mehta","year":"2020","unstructured":"Mehta, D., et al.: XNect: real-time multi-person 3d motion capture with a single RGB camera. ACM Trans. Graph. (TOG) 39(4), 82\u201391 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"30_CR30","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. (TOG) 36(4), 1\u20134 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"doi-asserted-by":"crossref","unstructured":"Moreno-Noguer, F.: 3d human pose estimation from a single image via distance matrix regression. In: Computer Vision and Pattern Recognition (CVPR) (2017)","key":"30_CR31","DOI":"10.1109\/CVPR.2017.170"},{"doi-asserted-by":"crossref","unstructured":"M\u00fcller, L., Osman, A.A.A., Tang, S., Huang, C.H.P., Black, M.J.: On self-contact and human pose. In: Computer Vision and Pattern Recognition (CVPR) (2021)","key":"30_CR32","DOI":"10.1109\/CVPR46437.2021.00986"},{"key":"30_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"doi-asserted-by":"crossref","unstructured":"Pavlakos, G., et al.: Expressive body capture: 3d hands, face, and body from a single image. In: Computer Vision and Pattern Recognition (CVPR) (2019)","key":"30_CR34","DOI":"10.1109\/CVPR.2019.01123"},{"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: Computer Vision and Pattern Recognition (CVPR) (2017)","key":"30_CR35","DOI":"10.1109\/CVPR.2017.139"},{"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: Computer Vision and Pattern Recognition (CVPR) (2018)","key":"30_CR36","DOI":"10.1109\/CVPR.2018.00055"},{"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: Computer Vision and Pattern Recognition (CVPR) (2019)","key":"30_CR37","DOI":"10.1109\/CVPR.2019.00794"},{"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: International Conference on Computer Vision (ICCV) (2021)","key":"30_CR38","DOI":"10.1109\/ICCV48922.2021.01129"},{"key":"30_CR39","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":"30_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/978-3-030-01249-6_46","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Rhodin","year":"2018","unstructured":"Rhodin, H., Salzmann, M., Fua, P.: Unsupervised geometry-aware representation for 3d human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Unsupervised geometry-aware representation learning for 3d human pose estimatio. LNCS, vol. 11214, pp. 765\u2013782. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_46"},{"doi-asserted-by":"crossref","unstructured":"Saini, S., Rambli, D.R.B.A., Sulaiman, S.B., Zakaria, M.N.B.: Human pose tracking in low-dimensional subspace using manifold learning by charting. In: International Conference on Signal and Image Processing Applications (ICSIPA) (2013)","key":"30_CR41","DOI":"10.1109\/ICSIPA.2013.6708014"},{"key":"30_CR42","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.proeng.2012.07.227","volume":"41","author":"S Saini","year":"2012","unstructured":"Saini, S., Rambli, D.R.B.A., Sulaiman, S.B., Zakaria, M.N.B., Rohkmah, S.: Markerless multi-view human motion tracking using manifold model learning by charting. Proc. Eng. 41, 664\u2013670 (2012)","journal-title":"Proc. Eng."},{"doi-asserted-by":"crossref","unstructured":"Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: PiFU: pixel-aligned implicit function for high-resolution clothed human digitization. In: International Conference on Computer Vision (ICCV) (2019)","key":"30_CR43","DOI":"10.1109\/ICCV.2019.00239"},{"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: International Conference on Computer Vision (ICCV) (2019)","key":"30_CR44","DOI":"10.1109\/ICCV.2019.00241"},{"issue":"1","key":"30_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3407659","volume":"40","author":"M Shi","year":"2020","unstructured":"Shi, M.: Motionet: 3d human motion reconstruction from monocular video with skeleton consistency. ACM Trans. Graph. (TOG) 40(1), 1\u201315 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"30_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459825","volume":"40","author":"S Shimada","year":"2021","unstructured":"Shimada, S., Golyanik, V., Xu, W., P\u00e9rez, P., Theobalt, C.: Neural monocular 3d human motion capture with physical awareness. ACM Trans. Graph. (TOG) 40(4), 1\u20135 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"6","key":"30_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3414685.3417877","volume":"39","author":"S Shimada","year":"2020","unstructured":"Shimada, S., Golyanik, V., Xu, W., Theobalt, C.: PhysCAP: physically plausible monocular 3d motion capture in real time. ACM Trans. Graph. 39(6), 1\u20136 (2020)","journal-title":"ACM Trans. Graph."},{"unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems (NIPS) (2015)","key":"30_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: International Conference on Computer Vision (ICCV) (2019)","key":"30_CR49","DOI":"10.1109\/ICCV.2019.00545"},{"doi-asserted-by":"crossref","unstructured":"Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., Fua, P.: Structured prediction of 3d human pose with deep neural networks. In: British Machine Vision Conference (BMVC) (2016)","key":"30_CR50","DOI":"10.5244\/C.30.130"},{"doi-asserted-by":"crossref","unstructured":"Tom\u00e8, D., Russell, C., Agapito, L.: Lifting from the deep: convolutional 3d pose estimation from a single image. In: Computer Vision and Pattern Recognition (CVPR) (2017)","key":"30_CR51","DOI":"10.1109\/CVPR.2017.603"},{"unstructured":"Vicon blade. https:\/\/www.vicon.com\/","key":"30_CR52"},{"doi-asserted-by":"crossref","unstructured":"Wang, J., Xu, H., Xu, J., Liu, S., Wang, X.: Synthesizing long-term 3d human motion and interaction in 3d scenes. In: Computer Vision and Pattern Recognition (CVPR) (2021)","key":"30_CR53","DOI":"10.1109\/CVPR46437.2021.00928"},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, L., Rathore, S., Shin, D., Fowlkes, C.: Geometric pose affordance: 3d human pose with scene constraints. In: Arxiv (2019)","key":"30_CR54","DOI":"10.1007\/978-3-031-25075-0_1"},{"key":"30_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-030-66096-3_36","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Shin, D., Fowlkes, C.C.: Predicting camera viewpoint improves cross-dataset generalization for 3d human pose estimation. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 523\u2013540. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66096-3_36"},{"issue":"4","key":"30_CR56","first-page":"1","volume":"29","author":"X Wei","year":"2010","unstructured":"Wei, X., Chai, J.: VideoMocap: modeling physically realistic human motion from monocular video sequences. ACM Trans. Graph. (TOG) 29(4), 1\u20137 (2010)","journal-title":"ACM Trans. Graph. (TOG)"},{"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: Computer Vision and Pattern Recognition (CVPR) (2018)","key":"30_CR57","DOI":"10.1109\/CVPR.2018.00551"},{"doi-asserted-by":"crossref","unstructured":"Yi, X., et al.: Physical inertial poser (PIP): physics-aware real-time human motion tracking from sparse inertial sensors. In: Computer Vision and Pattern Recognition (CVPR) (2022)","key":"30_CR58","DOI":"10.1109\/CVPR52688.2022.01282"},{"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: Computer Vision and Pattern Recognition (CVPR) (2021)","key":"30_CR59","DOI":"10.1109\/CVPR46437.2021.00708"},{"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: Computer Vision and Pattern Recognition (CVPR) (2018)","key":"30_CR60","DOI":"10.1109\/CVPR.2018.00229"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, Y., Bogo, F., Marc, P., Tang, S.: Learning motion priors for 4d human body capture in 3d scenes. In: International Conference on Computer Vision (ICCV), October 2021","key":"30_CR61","DOI":"10.1109\/ICCV48922.2021.01115"},{"doi-asserted-by":"crossref","unstructured":"Zhang, T., Huang, B., Wang, Y.: Object-occluded human shape and pose estimation from a single color image. In: Computer Vision and Pattern Recognition (CVPR) (2020)","key":"30_CR62","DOI":"10.1109\/CVPR42600.2020.00740"},{"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: International Conference on Computer Vision (ICCV) (2017)","key":"30_CR63","DOI":"10.1109\/ICCV.2017.51"},{"doi-asserted-by":"crossref","unstructured":"Zou, Y., Yang, J., Ceylan, D., Zhang, J., Perazzi, F., Huang, J.B.: Reducing footskate in human motion reconstruction with ground contact constraints. In: Winter Conference on Applications of Computer Vision (WACV) (2020)","key":"30_CR64","DOI":"10.1109\/WACV45572.2020.9093329"}],"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\/978-3-031-20047-2_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T15:02:05Z","timestamp":1678374125000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20047-2_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200465","9783031200472"],"references-count":64,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20047-2_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 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)"}}]}}