{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:38:49Z","timestamp":1792543129561,"version":"4.5.0"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197864","type":"print"},{"value":"9783031197871","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19787-1_30","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T18:16:11Z","timestamp":1666289771000},"page":"529-545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Motion and\u00a0Appearance Adaptation for\u00a0Cross-domain Motion Transfer"],"prefix":"10.1007","author":[{"given":"Borun","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhong","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiezheng","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuning","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"30_CR1","unstructured":"Adobe\u2019s mixamo website. https:\/\/www.mixamo.com Accessed 3 Nov 2021"},{"issue":"2","key":"30_CR2","first-page":"20","volume":"6","author":"B Amos","year":"2016","unstructured":"Amos, B., Ludwiczuk, B., Satyanarayanan, M., et al.: Openface: a general-purpose face recognition library with mobile applications. CMU School Comput. Sci. 6(2), 20 (2016)","journal-title":"CMU School Comput. Sci."},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Zhao, A., Dalca, A.V., Durand, F., Guttag, J.: Synthesizing images of humans in unseen poses. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8340\u20138348 (2018)","DOI":"10.1109\/CVPR.2018.00870"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Burkov, E., Pasechnik, I., Grigorev, A., Lempitsky, V.: Neural head reenactment with latent pose descriptors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13786\u201313795 (2020)","DOI":"10.1109\/CVPR42600.2020.01380"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Chan, C., Ginosar, S., Zhou, T., Efros, A.A.: Everybody dance now. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5933\u20135942 (2019)","DOI":"10.1109\/ICCV.2019.00603"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster R-CNN for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3339\u20133348 (2018)","DOI":"10.1109\/CVPR.2018.00352"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, C., Yuan, B., Tao, D.: Puppeteergan: arbitrary portrait animation with semantic-aware appearance transformation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13518\u201313527 (2020)","DOI":"10.1109\/CVPR42600.2020.01353"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Chu, T., Liu, Y., Deng, J., Li, W., Duan, L.: Denoised maximum classifier discrepancy for source-free unsupervised domain adaptation. In: Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) (2022)","DOI":"10.1609\/aaai.v36i1.19925"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Li, W., Chen, Y., Duan, L.: Unbiased mean teacher for cross-domain object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4091\u20134101 (2021)","DOI":"10.1109\/CVPR46437.2021.00408"},{"key":"30_CR10","unstructured":"Dong, J., Cong, Y., Sun, G., Fang, Z., Ding, Z.: Where and how to transfer: Knowledge aggregation-induced transferability perception for unsupervised domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 1\u20131 (2021)"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Dong, J., Cong, Y., Sun, G., Zhong, B., Xu, X.: What can be transferred: unsupervised domain adaptation for endoscopic lesions segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4022\u20134031, June 2020","DOI":"10.1109\/CVPR42600.2020.00408"},{"key":"30_CR12","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180\u20131189. PMLR (2015)"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Gu, K., Zhou, Y., Huang, T.: FLNET: landmark driven fetching and learning network for faithful talking facial animation synthesis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 10861\u201310868 (2020)","DOI":"10.1609\/aaai.v34i07.6717"},{"key":"30_CR14","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"30_CR15","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Huang, Z., Han, X., Xu, J., Zhang, T.: Few-shot human motion transfer by personalized geometry and texture modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2297\u20132306 (2021)","DOI":"10.1109\/CVPR46437.2021.00233"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"30_CR18","unstructured":"Jakab, T., Gupta, A., Bilen, H., Vedaldi, A.: Unsupervised learning of object landmarks through conditional image generation. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 4020\u20134031 (2018)"},{"key":"30_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/978-3-030-58586-0_18","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Jeon","year":"2020","unstructured":"Jeon, S., Nam, S., Oh, S.W., Kim, S.J.: Cross-identity motion transfer for arbitrary objects through pose-attentive video reassembling. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 292\u2013308. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_18"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Kappel, M., et al.: High-fidelity neural human motion transfer from monocular video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1541\u20131550 (2021)","DOI":"10.1109\/CVPR46437.2021.00159"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Li, C., Lee, G.H.: From synthetic to real: Unsupervised domain adaptation for animal pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1482\u20131491 (2021)","DOI":"10.1109\/CVPR46437.2021.00153"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, C., Loy, C.C.: Dense intrinsic appearance flow for human pose transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3693\u20133702 (2019)","DOI":"10.1109\/CVPR.2019.00381"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Liu, W., Piao, Z., Min, J., Luo, W., Ma, L., Gao, S.: Liquid warping GAN: a unified framework for human motion imitation, appearance transfer and novel view synthesis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5904\u20135913 (2019)","DOI":"10.1109\/ICCV.2019.00600"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Deng, J., Gao, X., Li, W., Duan, L.: Bapa-net: boundary adaptation and prototype alignment for cross-domain semantic segmentation. In: The IEEE International Conference on Computer Vision (ICCV), October 2021","DOI":"10.1109\/ICCV48922.2021.00868"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Y., Deng, J., Tao, J., Chu, T., Duan, L., Li, W.: Undoing the damage of label shift for cross-domain semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition(CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00691"},{"key":"30_CR26","unstructured":"Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: Pose guided person image generation. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 99\u2013108 (2018)","DOI":"10.1109\/CVPR.2018.00018"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Nagrani, A., Chung, J.S., Zisserman, A.: Voxceleb: a large-scale speaker identification dataset. arXiv preprint arXiv:1706.08612 (2017)","DOI":"10.21437\/Interspeech.2017-950"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Ren, Y., Yu, X., Chen, J., Li, T.H., Li, G.: Deep image spatial transformation for person image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7690\u20137699 (2020)","DOI":"10.1109\/CVPR42600.2020.00771"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Siarohin, A., Lathuili\u00e8re, S., Tulyakov, S., Ricci, E., Sebe, N.: Animating arbitrary objects via deep motion transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2377\u20132386 (2019)","DOI":"10.1109\/CVPR.2019.00248"},{"key":"30_CR31","unstructured":"Siarohin, A., Lathuili\u00e8re, S., Tulyakov, S., Ricci, E., Sebe, N.: First order motion model for image animation. In: Advances in Neural Information Processing Systems, vol. 32, pp. 7137\u20137147 (2019)"},{"key":"30_CR32","doi-asserted-by":"crossref","unstructured":"Siarohin, A., Woodford, O.J., Ren, J., Chai, M., Tulyakov, S.: Motion representations for articulated animation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13653\u201313662 (2021)","DOI":"10.1109\/CVPR46437.2021.01344"},{"key":"30_CR33","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"30_CR34","doi-asserted-by":"crossref","unstructured":"Tao, J., Wang, B., Xu, B., Ge, T., Jiang, Y., Li, W., Duan, L.: Structure-aware motion transfer with deformable anchor model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3637\u20133646 (2022)","DOI":"10.1109\/CVPR52688.2022.00362"},{"key":"30_CR35","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"issue":"1","key":"30_CR36","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/TAI.2020.3031581","volume":"1","author":"C Wang","year":"2020","unstructured":"Wang, C., Xu, C., Tao, D.: Self-supervised pose adaptation for cross-domain image animation. IEEE Transa Artif. Intell. 1(1), 34\u201346 (2020)","journal-title":"IEEE Transa Artif. Intell."},{"issue":"11","key":"30_CR37","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TPAMI.2008.222","volume":"31","author":"X Wang","year":"2008","unstructured":"Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1955\u20131967 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/978-3-030-01261-8_41","volume-title":"Computer Vision \u2013 ECCV 2018","author":"O Wiles","year":"2018","unstructured":"Wiles, O., Koepke, A.S., Zisserman, A.: X2Face: a network for controlling face generation using images, audio, and pose codes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 690\u2013706. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_41"},{"key":"30_CR39","doi-asserted-by":"crossref","unstructured":"Xu, B., et al.: Move as you like: image animation in e-commerce scenario. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2759\u20132761 (2021)","DOI":"10.1145\/3474085.3478550"},{"key":"30_CR40","unstructured":"Zablotskaia, P., Siarohin, A., Zhao, B., Sigal, L.: DWNET: dense warp-based network for pose-guided human video generation. arXiv preprint arXiv:1910.09139 (2019)"},{"key":"30_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhao, W., Guan, Z., Peng, X., Peng, J.: Keypoint-graph-driven learning framework for object pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1065\u20131073 (2021)","DOI":"10.1109\/CVPR46437.2021.00112"},{"key":"30_CR42","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"30_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., Bai, X.: Progressive pose attention transfer for person image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2347\u20132356 (2019)","DOI":"10.1109\/CVPR.2019.00245"}],"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\/978-3-031-19787-1_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:03:57Z","timestamp":1792541037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-19787-1_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197864","9783031197871"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19787-1_30","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":"21 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}