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Automatic colorization of anime line drawing has attracted much attention in recent years since it can substantially benefit the animation industry.
“Principal warps: Thin-plate splines and the decomposition of deformations,”
Fred L. Bookstein, · 1989
Earlier work this paper cites.
“Manga colorization,”
Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng, · 2006
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“Lazybrush: Flexible painting tool for hand-drawn cartoons,”
Daniel Sỳkora, John Dingliana, and Steven Collins, · 2009
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“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
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“Perceptual losses for real-time style transfer and super-resolution,”
Justin Johnson, Alexandre Alahi, and Li Fei-Fei, · 2016
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“Automatic cartoon colorization based on convolutional neural network,”
Domonkos Varga, Csaba Attila Szabo, and Tamas Sziranyi, · 2017
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“Arbitrary style transfer in real-time with adaptive instance normalization,”
Xun Huang and Serge Belongie, · 2017
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“Comicolorization: semi-automatic manga colorization,”
Chie Furusawa, Kazuyuki Hiroshiba, Keisuke Ogaki, and Yuri Odagiri, · 2017
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“Least squares generative adversarial networks,”
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley, · 2017
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“Image-to-image translation with conditional adversarial networks,”
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros, · 2017
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“User-guided deep anime line art colorization with conditional adversarial networks,”
Yuanzheng Ci, Xinzhu Ma, Zhihui Wang, Haojie Li, and Zhongxuan Luo, · 2018
Earlier work this paper cites.
“Anime sketch colorization paired dataset from danbooru,” \url
Taebum Kim, · 2018
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“Squeeze-and-excitation networks,”
Jie Hu, Li Shen, and Gang Sun, · 2018
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“Multimodal unsupervised image-to-image translation,”
Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz, · 2018
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“Spectral normalization for generative adversarial networks,”
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida, · 2018
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“Cbam: Convolutional block attention module,”
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon, · 2018
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“The unreasonable effectiveness of deep features as a perceptual metric,”
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang, · 2018
“Active colorization for cartoon line drawings,”
Shu-Yu Chen, Jia-Qi Zhang, Lin Gao, Yue He, Shihong Xia, Min Shi, and Fang-Lue Zhang, · 2020
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“Reference-based sketch image colorization using augmented-self reference and dense semantic correspondence,”
Junsoo Lee, Eungyeup Kim, Yunsung Lee, Dongjun Kim, Jaehyuk Chang, and Jaegul Choo, · 2020
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“Adeleine colorization,” \url
Adeleine, · 2021
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“Line art colorization with concatenated spatial attention,”
Mingcheng Yuan and Edgar Simo-Serra, · 2021
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“A2-fpn: Attention aggregation based feature pyramid network for instance segmentation,”
Miao Hu, Yali Li, Lu Fang, and Shengjin Wang, · 2021
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“Petalica paint,” \url
Petalica, · 2019
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“Tag2pix: Line art colorization using text tag with secat and changing loss,”
Hyunsu Kim, Ho Young Jhoo, Eunhyeok Park, and Sungjoo Yoo, · 2019
Cited alongside, same era.
“Language-based colorization of scene sketches,”
Changqing Zou, Haoran Mo, Chengying Gao, Ruofei Du, and Hongbo Fu, · 2019
Cited alongside, same era.
“Gray2colornet: Transfer more colors from reference image,”
Peng Lu, Jinbei Yu, Xujun Peng, Zhaoran Zhao, and Xiaojie Wang, · 2020
Cited alongside, same era.
Manoj Kumar, Dirk Weissenborn, and Nal Kalchbrenner, · 2021
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“Disentangled image colorization via global anchors,”
Menghan Xia, Wenbo Hu, Tien-Tsin Wong, and Jue Wang, · 2022
Closest in time.
“Unicolor: A unified framework for multi-modal colorization with transformer,”
Zhitong Huang, Nanxuan Zhao, and Jing Liao, · 2022
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“Eliminating gradient conflict in reference-based line-art colorization,”
Zekun Li, Zhengyang Geng, Zhao Kang, Wenyu Chen, and Yibo Yang, · 2022
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“Learning contrastive representation for semantic correspondence,”
Taihong Xiao, Sifei Liu, Shalini De Mello, Zhiding Yu, Jan Kautz, and Ming-Hsuan Yang, · 2022
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