{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:30:25Z","timestamp":1782282625155,"version":"3.54.5"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region, China","award":["CityU 11212119"],"award-info":[{"award-number":["CityU 11212119"]}]},{"name":"NSF China","award":["62172363, 61902334"],"award-info":[{"award-number":["62172363, 61902334"]}]},{"name":"Centre for Applied Computing and Interactive Media (ACIM) of School of Creative Media, CityU"},{"name":"National Key Research & Development Program of China","award":["2018YFE0100900"],"award-info":[{"award-number":["2018YFE0100900"]}]},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"crossref","award":["9667234, 7005176"],"award-info":[{"award-number":["9667234, 7005176"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>\n            Recent deep generative models allow real-time generation of hair images from sketch inputs. Existing solutions often require a user-provided binary mask to specify a target hair shape. This not only costs users extra labor but also fails to capture complicated hair boundaries. Those solutions usually encode hair structures via orientation maps, which, however, are not very effective to encode complex structures. We observe that colored hair sketches already implicitly define target hair shapes as well as hair appearance and are more flexible to depict hair structures than orientation maps. Based on these observations, we present\n            <jats:italic>SketchHairSalon<\/jats:italic>\n            , a two-stage framework for generating realistic hair images directly from freehand sketches depicting desired hair structure and appearance. At the first stage, we train a network to predict a hair matte from an input hair sketch, with an optional set of non-hair strokes. At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte. To make the networks in the two stages aware of long-term dependency of strokes, we apply self-attention modules to them. To train these networks, we present a new dataset containing thousands of annotated hair sketch-image pairs and corresponding hair mattes. Two efficient methods for sketch completion are proposed to automatically complete repetitive braided parts and hair strokes, respectively, thus reducing the workload of users. Based on the trained networks and the two sketch completion strategies, we build an intuitive interface to allow even novice users to design visually pleasing hair images exhibiting various hair structures and appearance via freehand sketches. The qualitative and quantitative evaluations show the advantages of the proposed system over the existing or alternative solutions.\n          <\/jats:p>","DOI":"10.1145\/3478513.3480502","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:29:20Z","timestamp":1639160960000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["SketchHairSalon"],"prefix":"10.1145","volume":"40","author":[{"given":"Chufeng","family":"Xiao","sequence":"first","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deng","family":"Yu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Han","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youyi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Fu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.2307\/1969218"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58523-5_22"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461990"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.131"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459760"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392386"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157170"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1384429.1384439"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00326"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00355"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376248"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766931"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661254"},{"key":"e_1_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Phillip Isola Jun-Yan Zhu Tinghui Zhou and Alexei A Efros. 2017. Image-to-Image Translation with Conditional Adversarial Networks. (2017).  Phillip Isola Jun-Yan Zhu Tinghui Zhou and Alexei A Efros. 2017. Image-to-Image Translation with Conditional Adversarial Networks. (2017).","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1661412.1618510"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295346"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1006\/cgip.1994.1042"},{"key":"e_1_2_2_22_1","first-page":"11450","article-title":"Natural image matting via guided contextual attention","volume":"34","author":"Li Yaoyi","year":"2020","unstructured":"Yaoyi Li and Hongtao Lu . 2020 . Natural image matting via guided contextual attention . In Proceedings of the American Association for Artificial Intelligence (AAAI) , Vol. 34. 11450 -- 11457 . Yaoyi Li and Hongtao Lu. 2020. Natural image matting via guided contextual attention. In Proceedings of the American Association for Artificial Intelligence (AAAI), Vol. 34. 11450--11457.","journal-title":"Proceedings of the American Association for Artificial Intelligence (AAAI)"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/1009379.1009657"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2018.02.001"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00747"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459872"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306305.3332370"},{"key":"e_1_2_2_28_1","volume-title":"Computer Graphics Forum (CGF)","author":"Qiu Haonan","unstructured":"Haonan Qiu , Chuan Wang , Hang Zhu , Xiao Zhu , Jinjin Gu , and Xiaoguang Han . 2019. Two-phase Hair Image Synthesis by Self-Enhancing Generative Model . In Computer Graphics Forum (CGF) , Vol. 38 . Wiley Online Library , 403--412. Haonan Qiu, Chuan Wang, Hang Zhu, Xiao Zhu, Jinjin Gu, and Xiaoguang Han. 2019. Two-phase Hair Image Synthesis by Self-Enhancing Generative Model. In Computer Graphics Forum (CGF), Vol. 38. Wiley Online Library, 403--412."},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.723"},{"key":"e_1_2_2_30_1","volume-title":"DeepSketchHair: Deep Sketch-based 3D Hair Modeling","author":"Shen Y","year":"2020","unstructured":"Y Shen , C Zhang , H Fu , K Zhou , and Y Zheng . 2020. DeepSketchHair: Deep Sketch-based 3D Hair Modeling . IEEE Transactions on Visualization and Computer Graphics (TVCG) ( 2020 ). Y Shen, C Zhang, H Fu, K Zhou, and Y Zheng. 2020. DeepSketchHair: Deep Sketch-based 3D Hair Modeling. IEEE Transactions on Visualization and Computer Graphics (TVCG) (2020)."},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392488"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417780"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417824"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_7"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186822.1073267"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_11"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3332165.3347876"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356511"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275090"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_15"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00515"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478513.3480502","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3478513.3480502","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:48Z","timestamp":1750191108000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478513.3480502"}},"subtitle":["deep sketch-based hair image synthesis"],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10.1145\/3478513.3480502"],"URL":"https:\/\/doi.org\/10.1145\/3478513.3480502","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"2021-12-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}