{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:27:04Z","timestamp":1755998824918,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030695378"},{"type":"electronic","value":"9783030695385"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69538-5_28","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T21:16:40Z","timestamp":1614201400000},"page":"459-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Local Facial Makeup Transfer via Disentangled Representation"],"prefix":"10.1007","author":[{"given":"Zhaoyang","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengwu","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: BeautyGAN: instance-level facial makeup transfer with deep generative adversarial network. In: ACM MM (2018)","DOI":"10.1145\/3240508.3240618"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Chang, H., Lu, J., Yu, F., Finkelstein, A.: PairedCycleGAN: asymmetric style transfer for applying and removing makeup. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00012"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H.J., Hui, K.M., Wang, S.Y., Tsao, L.W., Shuai, H.H., Cheng, W.H.: BeautyGlow: on-demand makeup transfer framework with reversible generative network. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01028"},{"key":"28_CR4","unstructured":"Sarfraz, M.S., Seibold, C., Khalid, H., Stiefelhagen, R.: Content and colour distillation for learning image translations with the spatial profile loss. In: BMVC (2019)"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Gu, Q., Wang, G., Chiu, M.T., Tai, Y.W., Tang, C.K.: LADN: local adversarial disentangling network for facial makeup and de-makeup. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01058"},{"key":"28_CR6","unstructured":"Zhang, H., Chen, W., He, H., Jin, Y.: Disentangled makeup transfer with generative adversarial network. arXiv preprint arXiv:1907.01144 (2019)"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.310"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M.Y., Belongie, S.J., Kautz, J.: Multimodal unsupervised image-to-image translation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Diverse image-to-image translation via disentangled representations. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01246-5_3"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Ma, L., Sun, Q., Georgoulis, S., Gool, L.V., Schiele, B., Fritz, M.: Disentangled person image generation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00018"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Lorenz, D., Bereska, L., Milbich, T., Ommer, B.: Unsupervised part-based disentangling of object shape and appearance. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01121"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Esser, P., Haux, J., Ommer, B.: Unsupervised robust disentangling of latent characteristics for image synthesis. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00279"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Tong, W.S., Tang, C.K., Brown, M.S., Xu, Y.Q.: Example-based cosmetic transfer. In: Proceedings of the Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2007 (2007)","DOI":"10.1109\/PG.2007.31"},{"key":"28_CR14","unstructured":"Guo, D., Sim, T.: Digital face makeup by example. In: CVPR (2009)"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Li, C., Zhou, K., Lin, S.: Simulating makeup through physics-based manipulation of intrinsic image layers. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299093"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"28_CR17","unstructured":"Liu, S., Ou, X., Qian, R., Wang, W., Cao, X.: Makeup like a superstar: deep localized makeup transfer network. In: IJCAI (2016)"},{"key":"28_CR18","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: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z.: Multi-class generative adversarial networks with the L2 loss function. arXiv preprint arXiv:1611.04076 (2016)","DOI":"10.1109\/ICCV.2017.304"},{"key":"28_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"28_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Li, C., Wand, M.: Precomputed real-time texture synthesis with Markovian generative adversarial networks. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46487-9_43"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69538-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T22:01:11Z","timestamp":1671400871000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69538-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695378","9783030695385"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69538-5_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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":"3","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.","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)"}}]}}