{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:18:23Z","timestamp":1753888703760,"version":"3.41.2"},"reference-count":41,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T00:00:00Z","timestamp":1696032000000},"content-version":"vor","delay-in-days":272,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171057","62101064","62201072","62071067","62001054"],"award-info":[{"award-number":["62171057","62101064","62201072","62071067","62001054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Artistic portrait drawing (APDrawing) generation has seen progress in recent years. However, due to the naturally high scarcity and artistry, it is difficult to collect large\u2010scale labeled and paired data and generally divide drawing styles into several specific recognized categories. Existing works suffer from the limited labeled data and naive manual division of drawing styles according to the corresponding artists. They cannot adapt to the actual situations, for example, a single artist might have multiple drawing styles and APDrawings from different artists might share similar styles. In this paper, we propose to use unlabeled and unpaired data and perform the task in an unsupervised manner. Without manual division of drawing styles, we take each portrait drawing as a unique style and introduce self\u2010supervised feature learning to learn free styles for unlabeled portrait drawings. Besides, we devise a style bank and a decoupled cycle structure to take over two main considerations in the task: generation quality and style control. Extensive experiments show that our model is more adaptable to different style inputs than state\u2010of\u2010the\u2010art methods.<\/jats:p>","DOI":"10.1155\/2023\/9601261","type":"journal-article","created":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T01:20:13Z","timestamp":1696123213000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Portrait Drawing Generation for Free Styles"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7554-4502","authenticated-orcid":false,"given":"Ming","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1486-0573","authenticated-orcid":false,"given":"Jianxin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2182-2228","authenticated-orcid":false,"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-4624","authenticated-orcid":false,"given":"Qi","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-7422","authenticated-orcid":false,"given":"Haifeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3345-1732","authenticated-orcid":false,"given":"Zirui","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0184-5965","authenticated-orcid":false,"given":"Cong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"700","article-title":"Unsupervised image-to-image translation networks","author":"Liu M. Y.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","unstructured":"YiZ. ZhangH. TanP. andGongM. Dualgan: unsupervised dual learning for image-to-image translation Proceedings of the IEEE International Conference on Computer Vision October 2017 Venice Italy 2849\u20132857.","DOI":"10.1109\/ICCV.2017.310"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.08.075"},{"key":"e_1_2_9_4_2","unstructured":"ChampandardA. J. Semantic style transfer and turning two-bit doodles into fine artworks 2016 https:\/\/arxiv.org\/abs\/1603.01768."},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"LiX. LiuS. KautzJ. andYangM. H. Learning linear transformations for fast image and video style transfer Proceedings of the IEEE conference on computer vision and pattern recognition June 2019 Long Beach CA USA 3809\u20133817 https:\/\/doi.org\/10.1109\/CVPR.2019.00393.","DOI":"10.1109\/CVPR.2019.00393"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04928-1"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04253-2"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05213-x"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.02.075"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.08.048"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.085"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.044"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.02.054"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22726"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22372"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22356"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"ZhuJ. Y. ParkT. IsolaP. andEfrosA. A. Unpaired image-to-image translation using cycle-consistent adversarial networks Proceedings of the IEEE international conference on computer vision October 2017 Venice Italy 2223\u20132232 https:\/\/doi.org\/10.1109\/ICCV.2017.244 2-s2.0-85041892358.","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"GatysL. A. EckerA. S. andBethgeM. Image style transfer using convolutional neural networks Proceedings of the IEEE conference on computer vision and pattern recognition June 2016 Las Vegas NV USA 2414\u20132423.","DOI":"10.1109\/CVPR.2016.265"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"LiC.andWandM. Combining Markov random fields and convolutional neural networks for image synthesis Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA 2479\u20132486.","DOI":"10.1109\/CVPR.2016.272"},{"key":"e_1_2_9_20_2","unstructured":"UlyanovD. LebedevV. VedaldiA. andLempitskyV. S. Texture networks: feed-forward synthesis of textures and stylized images June 2016 New York City NY USA ICML."},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"YiR. LiuY. J. LaiY. K. andRosinP. L. ApdrawingGAN: generating artistic portrait drawings from face photos with hierarchical GANs Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition January 2019 Long Beach CA USA 10743\u201310752 https:\/\/doi.org\/10.1109\/CVPR.2019.01100.","DOI":"10.1109\/CVPR.2019.01100"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"YiR. LiuY. J. LaiY. K. andRosinP. L. Unpaired portrait drawing generation via asymmetric cycle mapping Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition June 2020 Seattle WA USA 8217\u20138225 https:\/\/doi.org\/10.1109\/CVPR42600.2020.00824.","DOI":"10.1109\/CVPR42600.2020.00824"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"YuanM.andSimo-SerraE. Line art colorization with concatenated spatial attention Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition June 2021 Nashville TN USA 3946\u20133950 https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00442.","DOI":"10.1109\/CVPRW53098.2021.00442"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"ZhangL. JiangJ. JiY. andLiuC. Smartshadow: artistic shadow drawing tool for line drawings Proceedings of the IEEE\/CVF International Conference on Computer Vision October 2021 Montreal Canada 5391\u20135400.","DOI":"10.1109\/ICCV48922.2021.00534"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"ZhangL. LiC. Simo-SerraE. JiY. WongT. T. andLiuC. User-guided line art flat filling with split filling mechanism Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition June 2021 Nashville TN USA 9889\u20139898 https:\/\/doi.org\/10.1109\/CVPR46437.2021.00976.","DOI":"10.1109\/CVPR46437.2021.00976"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"LiY. FangC. HertzmannA. ShechtmanE. andYangM. H. Im2pencil: controllable pencil illustration from photographs Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition June 2019 Long Beach CA USA 1525\u20131534.","DOI":"10.1109\/CVPR.2019.00162"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"HertzmannA. JacobsC. E. OliverN. CurlessB. andSalesinD. H. Image analogies Proceedings of the 28th annual conference on Computer graphics and interactive techniques August 2001 New York NY USA 327\u2013340.","DOI":"10.1145\/383259.383295"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"IsolaP. ZhuJ. Y. ZhouT. andEfrosA. A. Image-to-image translation with conditional adversarial networks Proceedings of the IEEE conference on computer vision and pattern recognition July 2017 Honolulu HI USA 1125\u20131134.","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_9_29_2","doi-asserted-by":"crossref","unstructured":"ChoiY. ChoiM. KimM. HaJ. W. KimS. andChooJ. Stargan: unified generative adversarial networks for multi-domain image-to-image translation Proceedings of the IEEE conference on computer vision and pattern recognition June 2018 Salt Lake City UT USA 8789\u20138797.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"ChoiY. UhY. YooJ. andHaJ. W. Stargan v2: diverse image synthesis for multiple domains Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition June 2020 Seattle WA USA 8188\u20138197.","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"AnooshehA. AgustssonE. TimofteR. andVan GoolL. Combogan: unrestrained scalability for image domain translation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops December 2018 Salt Lake City UT USA 783\u2013790.","DOI":"10.1109\/CVPRW.2018.00122"},{"key":"e_1_2_9_32_2","doi-asserted-by":"crossref","unstructured":"HuangX. LiuM. Y. BelongieS. andKautzJ. Multimodal unsupervised image-to-image translation Proceedings of the European Conference on Computer Vision September 2018 Munich Germany 172\u2013189.","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925968"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"LuM. ZhaoH. YaoA. XuF. ChenY. andZhangL. Decoder network over lightweight reconstructed feature for fast semantic style transfer Proceedings of the IEEE international conference on computer vision October 2017 Venice Italy 2469\u20132477.","DOI":"10.1109\/ICCV.2017.270"},{"volume-title":"Journal of Visual Communication and Image Representation","year":"2022","author":"Mallika","key":"e_1_2_9_35_2"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05630-y"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.064"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.04.027"},{"key":"e_1_2_9_39_2","doi-asserted-by":"crossref","unstructured":"JohnsonJ. AlahiA. andFei-FeiL. Perceptual losses for real-time style transfer and super-resolution 2016 https:\/\/arxiv.org\/abs\/1603.08155.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_2_9_40_2","unstructured":"KarrasT. AilaT. LaineS. andLehtinenJ. Progressive growing of gans for improved quality stability and variation 2017 https:\/\/arxiv.org\/abs\/1710.10196."},{"key":"e_1_2_9_41_2","doi-asserted-by":"crossref","unstructured":"YuC. WangJ. PengC. GaoC. YuG. andSangN. Bisenet: bilateral segmentation network for real-time semantic segmentation Proceedings of the European conference on computer vision September 2018 Munich Germany 325\u2013341.","DOI":"10.1007\/978-3-030-01261-8_20"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/9601261.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/9601261.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/9601261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:31:23Z","timestamp":1735623083000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/9601261"}},"subtitle":[],"editor":[{"given":"Mohammad R.","family":"Khosravi","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/9601261"],"URL":"https:\/\/doi.org\/10.1155\/2023\/9601261","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"type":"print","value":"0884-8173"},{"type":"electronic","value":"1098-111X"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2023-03-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9601261"}}