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In this work, we propose EMS, the first learning-based framework for single-view 3D eyebrow reconstruction. Following the methods of scalp hair reconstruction, we also represent the eyebrow as a set of fiber curves and convert the reconstruction to fibers growing problem. Three modules are then carefully designed:\n            <jats:italic toggle=\"yes\">RootFinder<\/jats:italic>\n            firstly localizes the fiber root positions which indicate where to grow;\n            <jats:italic toggle=\"yes\">OriPredictor<\/jats:italic>\n            predicts an orientation field in the 3D space to guide the growing of fibers;\n            <jats:italic toggle=\"yes\">FiberEnder<\/jats:italic>\n            is designed to determine when to stop the growth of each fiber. Our\n            <jats:italic toggle=\"yes\">OriPredictor<\/jats:italic>\n            directly borrows the method used in hair reconstruction. Considering the differences between hair and eyebrows, both\n            <jats:italic toggle=\"yes\">RootFinder<\/jats:italic>\n            and\n            <jats:italic toggle=\"yes\">FiberEnder<\/jats:italic>\n            are newly proposed. Specifically, to cope with the challenge that the root location is severely occluded, we formulate root localization as a density map estimation task. Given the predicted density map, a density-based clustering method is further used for finding the roots. For each fiber, the growth starts from the root point and moves step by step until the ending, where each step is defined as an oriented line segment with a constant length according to the predicted orientation field. To determine when to end, a pixel-aligned RNN architecture is designed to form a binary classifier, which outputs stop or not for each growing step. To support the training of all proposed networks, we build the first 3D synthetic eyebrow dataset that contains 400 high-quality eyebrow models manually created by artists. Extensive experiments have demonstrated the effectiveness of the proposed\n            <jats:italic toggle=\"yes\">EMS<\/jats:italic>\n            pipeline on a variety of different eyebrow styles and lengths, ranging from short and sparse to long bushy eyebrows.\n          <\/jats:p>","DOI":"10.1145\/3618323","type":"journal-article","created":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T10:20:48Z","timestamp":1701771648000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["EMS: 3D Eyebrow Modeling from Single-View Images"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0604-7421","authenticated-orcid":false,"given":"Chenghong","family":"Li","sequence":"first","affiliation":[{"name":"FNii, CUHKSZ, China and SSE, CUHKSZ, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1440-9096","authenticated-orcid":false,"given":"Leyang","family":"Jin","sequence":"additional","affiliation":[{"name":"SSE, CUHKSZ, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7784-8323","authenticated-orcid":false,"given":"Yujian","family":"Zheng","sequence":"additional","affiliation":[{"name":"FNii, CUHKSZ, China and SSE, CUHKSZ, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0470-5548","authenticated-orcid":false,"given":"Yizhou","family":"Yu","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0162-3296","authenticated-orcid":false,"given":"Xiaoguang","family":"Han","sequence":"additional","affiliation":[{"name":"SSE, CUHKSZ, China and FNii, CUHKSZ, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,5]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3472954"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185613"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925962"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661285"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766924"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925961"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461990"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185612"},{"key":"e_1_2_2_9_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. 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