{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:25:29Z","timestamp":1765268729539,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030586201"},{"type":"electronic","value":"9783030586218"}],"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\/978-3-030-58621-8_11","type":"book-chapter","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T19:03:23Z","timestamp":1606417403000},"page":"178-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Hierarchical Style-Based Networks for Motion Synthesis"],"prefix":"10.1007","author":[{"given":"Jingwei","family":"Xu","sequence":"first","affiliation":[]},{"given":"Huazhe","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Bingbing","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Trevor","family":"Darrell","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Barsoum, E., Kender, J., Liu, Z.: HP-GAN: probabilistic 3D human motion prediction via GAN. In: CVPR Workshops, pp. 1418\u20131427 (2018)","DOI":"10.1109\/CVPRW.2018.00191"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Brand, M., Hertzmann, A.: Style machines. In: SIGGRAPH, pp. 183\u2013192 (2000)","DOI":"10.1145\/344779.344865"},{"issue":"5","key":"11_CR3","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.1137\/0916069","volume":"16","author":"RH Byrd","year":"1995","unstructured":"Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5), 1190\u20131208 (1995)","journal-title":"SIAM J. Sci. Comput."},{"key":"11_CR4","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":"11_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C., et al.: Unsupervised 3D pose estimation with geometric self-supervision. In: CVPR, pp. 5714\u20135724 (2019)","DOI":"10.1109\/CVPR.2019.00586"},{"key":"11_CR6","unstructured":"Finn, C., Goodfellow, I.J., Levine, S.: Unsupervised learning for physical interaction through video prediction. In: NIPS, pp. 64\u201372 (2016)"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR, pp. 2414\u20132423 (2016)","DOI":"10.1109\/CVPR.2016.265"},{"issue":"8","key":"11_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"4","key":"11_CR9","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MCG.2017.3271464","volume":"37","author":"D Holden","year":"2017","unstructured":"Holden, D., Habibie, I., Kusajima, I., Komura, T.: Fast neural style transfer for motion data. IEEE Comput. Graph. Appl. 37(4), 42\u201349 (2017)","journal-title":"IEEE Comput. Graph. Appl."},{"issue":"4","key":"11_CR10","doi-asserted-by":"publisher","first-page":"42:1","DOI":"10.1145\/3072959.3073663","volume":"36","author":"D Holden","year":"2017","unstructured":"Holden, D., Komura, T., Saito, J.: Phase-functioned neural networks for character control. ACM Trans. Graph. 36(4), 42:1\u201342:13 (2017)","journal-title":"ACM Trans. Graph."},{"issue":"4","key":"11_CR11","doi-asserted-by":"publisher","first-page":"138:1","DOI":"10.1145\/2897824.2925975","volume":"35","author":"D Holden","year":"2016","unstructured":"Holden, D., Saito, J., Komura, T.: A deep learning framework for character motion synthesis and editing. ACM Trans. Graph. 35(4), 138:1\u2013138:11 (2016)","journal-title":"ACM Trans. Graph."},{"key":"11_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"11_CR13","unstructured":"Kovar, L., Gleicher, M.: Flexible automatic motion blending with registration curves. In: SIGGRAPH\/Eurographics Symposium, pp. 214\u2013224 (2003)"},{"key":"11_CR14","unstructured":"Kulkarni, T.D., Narasimhan, K., Saeedi, A., Tenenbaum, J.: Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. In: NIPS, pp. 3675\u20133683 (2016)"},{"key":"11_CR15","unstructured":"Lee, Y., Sun, S., Somasundaram, S., Hu, E.S., Lim, J.J.: Composing complex skills by learning transition policies. In: ICLR (2019)"},{"issue":"4","key":"11_CR16","doi-asserted-by":"publisher","first-page":"28:1","DOI":"10.1145\/2185520.2185524","volume":"31","author":"S Levine","year":"2012","unstructured":"Levine, S., Wang, J.M., Haraux, A., Popovic, Z., Koltun, V.: Continuous character control with low-dimensional embeddings. ACM Trans. Graph. 31(4), 28:1\u201328:10 (2012)","journal-title":"ACM Trans. Graph."},{"issue":"3","key":"11_CR17","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1145\/566654.566604","volume":"21","author":"Y Li","year":"2002","unstructured":"Li, Y., Wang, T., Shum, H.: Motion texture: a two-level statistical model for character motion synthesis. ACM Trans. Graph. 21(3), 465\u2013472 (2002)","journal-title":"ACM Trans. Graph."},{"key":"11_CR18","unstructured":"Li, Y., Roblek, D., Tagliasacchi, M.: From here to there: video inbetweening using direct 3D convolutions. CoRR abs\/1905.10240 (2019)"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yeh, R.A., Tang, X., Liu, Y., Agarwala, A.: Video frame synthesis using deep voxel flow. In: ICCV, pp. 4473\u20134481 (2017)","DOI":"10.1109\/ICCV.2017.478"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Meyer, S., Wang, O., Zimmer, H., Grosse, M., Sorkine-Hornung, A.: Phase-based frame interpolation for video. In: CVPR, pp. 1410\u20131418 (2015)","DOI":"10.1109\/CVPR.2015.7298747"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Myers, D.R.: Robot Motion: Planning and Control edited by Michael Brady M.I.T. Press, Cambridge MA, USA, 1983 (\u00a333.95). Robotica 1(2), 109 (1983)","DOI":"10.1017\/S0263574700001260"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive separable convolution. In: ICCV, pp. 261\u2013270 (2017)","DOI":"10.1109\/ICCV.2017.37"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Park, S.I., Shin, H.J., Shin, S.Y.: On-line locomotion generation based on motion blending. In: SIGGRAPH\/Eurographics Symposium, pp. 105\u2013111 (2002)","DOI":"10.1145\/545261.545279"},{"key":"11_CR24","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS, pp. 8024\u20138035 (2019)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Auli, M.: Modeling human motion with quaternion-based neural networks. In: IJCV (2019)","DOI":"10.1007\/s11263-019-01245-6"},{"issue":"4","key":"11_CR26","first-page":"143","volume":"37","author":"XB Peng","year":"2018","unstructured":"Peng, X.B., Abbeel, P., Levine, S., van de Panne, M.: DeepMimic: example-guided deep reinforcement learning of physics-based character skills. ACM Trans. Graph. (TOG) 37(4), 143 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Shoemake, K.: Animating rotation with quaternion curves. In: SIGGRAPH, pp. 245\u2013254 (1985)","DOI":"10.1145\/325165.325242"},{"issue":"3\u20134","key":"11_CR28","first-page":"215","volume":"23","author":"CI Tan","year":"2012","unstructured":"Tan, C.I., Tai, W.: Characteristics preserving racer animation: a data-driven race path synthesis in formation space. J. Vis. Comput. Anim. 23(3\u20134), 215\u2013223 (2012)","journal-title":"J. Vis. Comput. Anim."},{"key":"11_CR29","unstructured":"Tenenbaum, J.B., Freeman, W.T.: Separating style and content. In: NeurIPS, pp. 662\u2013668 (1996)"},{"key":"11_CR30","unstructured":"Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., Gelly, S.: Towards accurate generative models of video: a new metric & challenges. CoRR abs\/1812.01717 (2018). http:\/\/arxiv.org\/abs\/1812.01717"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Urtasun, R., Fleet, D.J., Geiger, A., Popovic, J., Darrell, T., Lawrence, N.D.: Topologically-constrained latent variable models. In: ICML, pp. 1080\u20131087 (2008)","DOI":"10.1145\/1390156.1390292"},{"issue":"3","key":"11_CR32","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TPAMI.2007.60","volume":"29","author":"Y Wexler","year":"2007","unstructured":"Wexler, Y., Shechtman, E., Irani, M.: Space-time completion of video. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 463\u2013476 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"11_CR33","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.1109\/TII.2019.2894113","volume":"15","author":"G Xia","year":"2019","unstructured":"Xia, G., Sun, H., Liu, Q., Hang, R.: Learning-based sphere nonlinear interpolation for motion synthesis. IEEE Trans. Ind. Inform. 15(5), 2927\u20132937 (2019). https:\/\/doi.org\/10.1109\/TII.2019.2894113","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"4","key":"11_CR34","doi-asserted-by":"publisher","first-page":"119:1","DOI":"10.1145\/2766999","volume":"34","author":"S Xia","year":"2015","unstructured":"Xia, S., Wang, C., Chai, J., Hodgins, J.K.: Realtime style transfer for unlabeled heterogeneous human motion. ACM Trans. Graph. 34(4), 119:1\u2013119:10 (2015)","journal-title":"ACM Trans. Graph."},{"key":"11_CR35","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":"11_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, H., Starke, S., Komura, T., Saito, J.: Mode-adaptive neural networks for quadruped motion control. ACM Trans. Graph. 37(4)","DOI":"10.1145\/3197517.3201366"}],"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\/978-3-030-58621-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:04:49Z","timestamp":1732579489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58621-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586201","9783030586218"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58621-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"27 November 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)"}}]}}