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Graph."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n            We introduce a deep learning based framework for modeling dynamic hairs from monocular videos, which could be captured by a commodity video camera or downloaded from Internet. The framework mainly consists of two neural networks, i.e.,\n            <jats:italic>HairSpatNet<\/jats:italic>\n            for inferring 3D spatial features of hair geometry from 2D image features, and\n            <jats:italic>HairTempNet<\/jats:italic>\n            for extracting temporal features of hair motions from video frames. The spatial features are represented as 3D occupancy fields depicting the hair volume shapes and 3D orientation fields indicating the hair growing directions. The temporal features are represented as bidirectional 3D warping fields, describing the forward and backward motions of hair strands cross adjacent frames. Both\n            <jats:italic>HairSpatNet<\/jats:italic>\n            and\n            <jats:italic>HairTempNet<\/jats:italic>\n            are trained with synthetic hair data. The spatial and temporal features predicted by the networks are subsequently used for growing hair strands with both spatial and temporal consistency. Experiments demonstrate that our method is capable of constructing plausible dynamic hair models that closely resemble the input video, and compares favorably to previous single-view techniques.\n          <\/jats:p>","DOI":"10.1145\/3355089.3356511","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T20:27:58Z","timestamp":1573244878000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Dynamic hair modeling from monocular videos using deep neural networks"],"prefix":"10.1145","volume":"38","author":[{"given":"Lingchen","family":"Yang","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zefeng","family":"Shi","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youyi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang University and ZJU-FaceUnity Joint Lab of Intelligent Graphics, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University and ZJU-FaceUnity Joint Lab of Intelligent Graphics, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0667-3"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818112"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925961"},{"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":"10.1145\/2185520.2185612"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.126"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601133"},{"key":"e_1_2_2_8_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. 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