{"type":"ScholarlyArticle","title":"Re:Draw - Context Aware Translation as a Controllable Method for Artistic Production","authors":[{"name":"João Libório Cardoso","slug":"joao-liborio-cardoso","given":"João Libório","family":"Cardoso","affiliation":"TU Wien , Austria"},{"name":"Francesco Banterle","slug":"francesco-banterle","given":"Francesco","family":"Banterle","affiliation":"CNR-ISTI , Italy"},{"name":"Paolo Cignoni","slug":"paolo-cignoni","given":"Paolo","family":"Cignoni","affiliation":"CNR-ISTI , Italy"},{"name":"Michael Wimmer","slug":"michael-wimmer","given":"Michael","family":"Wimmer","affiliation":"TU Wien , Austria"}],"abstract":"We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance -- where existing methods fall short. By doing so, our method opens new avenues for the controllable use of AI within artistic creation, from animation to digital art.\r\n\r\nAs an use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications -- eyes serve as a focal point that captures viewer attention and conveys a range of emotions; however, the labor-intensive nature of traditional animation often leads to compromises in the complexity and consistency of eye design. Furthermore, we remove the need for production data for training and introduce a new character recognition method that surpasses existing work by not requiring fine-tuning to specific productions. This proposed use case could help maintain consistency throughout production and unlock bolder and more detailed design choices without the production cost drawbacks. A user study shows context-aware translation is preferred over existing work 95.16% of the time.","doi":"10.24963/ijcai.2024/842","url":"https://ijcai.org/proceedings/2024/842","pdf":"https://ijcai.org/proceedings/2024/0842.pdf","markdown":"https://ijcai.org/proceedings/2024/842.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/842","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":842,"pages":{"first":7609,"last":7617},"keywords":["Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Application domains: Images, movies and visual arts","Application domains: Computer Graphics and Animation","Methods and resources: AI systems for collaboration and co-creation","Theory and philosophy of arts and creativity in AI systems: Social (multi-agent) creativity and human-computer co-creation"],"videos":[],"references":[{"position":1,"raw":"[Akita et al., 2020] Kenta Akita, Yuki Morimoto, and Reiji Tsu- runo. Deep-Eyes: Fully Automatic Anime Character Coloriza- tion with Painting of Details on Empty Pupils. In Alexander Wilkie and Francesco Banterle, editors, Eurographics 2020 - Short Papers. The Eurographics Association, 2020.","title":"Deep-Eyes: Fully Automatic Anime Character Colorization with Painting of Details on Empty Pupils","authors":"Kenta Akita, Yuki Morimoto, Reiji Tsuruno","year":"2020","venue":"Eurographics 2020 -Short Papers","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Balaban, 2015] Stephen Balaban. Deep learning and face recogni- tion: the state of the art. Biometric and surveillance technology for human and activity identification XII, 9457:68-75, 2015.","title":"Deep learning and face recognition: the state of the art","authors":"Stephen Balaban","year":"2015","venue":"Biometric and surveillance technology for human and activity identification","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Balntas et al., 2016] Vassileios Balntas, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk. Learning local feature descriptors with triplets and shallow convolutional neural networks. In Bmvc, volume 1, page 3, 2016.","title":"Learning local feature descriptors with triplets and shallow convolutional neural networks","authors":"Balntas","year":"2016","venue":"Bmvc","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Branwen et al., 2022] Gwern Branwen, Anonymous, and Dan- booru community. Danbooru2021: A large-scale crowdsourced and tagged anime illustration dataset. https://www.gwern.net/ Danbooru2021, 1 2022. Accessed: DATE.","title":"Gwern Branwen, Anonymous, and Danbooru community. Danbooru2021: A large-scale crowdsourced and tagged anime illustration dataset","authors":"Branwen","year":"2022","venue":null,"doi":null,"url":"https://www.gwern.net/Danbooru2021","internal_url":null},{"position":5,"raw":"[Ci et al., 2018] Yuanzheng Ci, Xinzhu Ma, Zhihui Wang, Haojie Li, and Zhongxuan Luo. User-guided deep anime line art col- orization with conditional adversarial networks. In Proceedings of the 26th ACM International Conference on Multimedia, MM '18, page 1536-1544, New York, NY, USA, 2018. Association for Computing Machinery.","title":"User-guided deep anime line art colorization with conditional adversarial networks","authors":"Ci","year":"2018","venue":"Proceedings of the 26th ACM International Conference on Multimedia, MM '18","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Coolkyousinnjya and Dragon Life Improvement Committee, 2017] Coolkyousinnjya and Dragon Life Improvement Committee. Miss kobayashi's dragon maid, 2017. [Online; accessed 4-August-2023].","title":"Coolkyousinnjya and Dragon Life Improvement Committee. Miss kobayashi's dragon maid","authors":null,"year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[David, 1988] H. A. David. The Method of Paired Comparisons, 2nd ed. Oxford University Press, 1988.","title":"The Method of Paired Comparisons","authors":"H A David","year":"1988","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Furansujin Connection, 2016] Furansujin Connection. Les étapes de fabrication. https://web.archive.org/web/20220401075414/ http://www.furansujinconnection.com/les-etapes-de-fabrication/ ., 2016. [Online; accessed 2022-March-21].","title":"Furansujin Connection. Les étapes de fabrication","authors":null,"year":"2016","venue":null,"doi":null,"url":"https://web.archive.org/web/20220401075414/http://www.furansujinconnection.com/les-etapes-de-fabrication/.,2016","internal_url":null},{"position":9,"raw":"[Gatys et al., 2016] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neu- ral networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27- 30, 2016, pages 2414-2423. IEEE Computer Society, 2016.","title":"Image style transfer using convolutional neural networks","authors":"Leon A Gatys, Alexander S Ecker, Matthias Bethge","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hermans et al., 2017] Alexander Hermans, Lucas Beyer, and Bas- tian Leibe. In defense of the triplet loss for person re- identification. arXiv preprint arXiv:1703.07737, 2017.","title":"In defense of the triplet loss for person reidentification","authors":"Alexander Hermans, Lucas Beyer, Bastian Leibe","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Huang and Belongie, 2017] Xun Huang and Serge J. Belongie. Ar- bitrary style transfer in real-time with adaptive instance normal- ization. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pages 1510- 1519. IEEE Computer Society, 2017.","title":"Arbitrary style transfer in real-time with adaptive instance normalization","authors":"Xun Huang, Serge J Belongie","year":"2017","venue":"IEEE International Conference on Computer Vision, ICCV 2017","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2018] Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz. Multimodal unsupervised image-to-image trans- lation. In Proceedings of the European Conference on Computer Vision (ECCV), 9 2018.","title":"Multimodal unsupervised image-to-image translation","authors":"Xun Huang, Ming-Yu Liu, Serge Belongie, Jan Kautz","year":"2018","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Karras et al., 2019] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial net- works. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 4401-4410. Computer Vision Foundation / IEEE, 2019.","title":"A style-based generator architecture for generative adversarial networks","authors":"Tero Karras, Samuli Laine, Timo Aila","year":"2019","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Karras et al., 2020] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 8107-8116. Computer Vision Foundation / IEEE, 2020.","title":"Analyzing and improving the image quality of stylegan","authors":"Karras","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Karras et al., 2021a] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. In Marc'Aurelio Ran- zato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Infor- mation Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 852-863, 2021.","title":"Alias-free generative adversarial networks","authors":"Karras","year":"2021","venue":"Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Karras et al., 2021b] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial net- works. IEEE Trans. Pattern Anal. Mach. Intell., 43(12):4217- 4228, 2021.","title":"A style-based generator architecture for generative adversarial networks","authors":"Karras","year":"2021","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kim et al., 2019] Hyunsu Kim, Ho Young Jhoo, Eunhyeok Park, and Sungjoo Yoo. Tag2pix: Line art colorization using text tag with secat and changing loss. In 2019 IEEE/CVF Interna- tional Conference on Computer Vision, ICCV 2019, Seoul, Ko- rea (South), October 27 -November 2, 2019, pages 9055-9064. IEEE, 2019.","title":"Tag2pix: Line art colorization using text tag with secat and changing loss","authors":"Kim","year":"2019","venue":"IEEE/CVF International Conference on Computer Vision, ICCV 2019","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kim et al., 2020] Junho Kim, Minjae Kim, Hyeonwoo Kang, and Kwang Hee Lee. U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image- to-image translation. In International Conference on Learning Representations, 2020. [LahIntheFutureland, 2023] LahIntheFutureland. Myste- riousv4 sdxl 1.0. https://civitai.com/models/118441/ lah-mysterious-or-sdxl, 2023. [Online; accessed 19-October- 2023].","title":"U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for imageto-image translation","authors":"Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwang Hee, Lee","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":"https://civitai.com/models/118441/lah-mysterious-or-sdxl,2023","internal_url":null},{"position":20,"raw":"[Lee et al., 2019] Gayoung Lee, Dohyun Kim, Youngjoon Yoo, Dongyoon Han, Jung-Woo Ha, and Jaehyuk Chang. Unpaired sketch-to-line translation via synthesis of sketches. In SIG- GRAPH Asia 2019 Technical Briefs, SA '19, page 45-48, New York, NY, USA, 2019. Association for Computing Machinery.","title":"Unpaired sketch-to-line translation via synthesis of sketches","authors":"Lee","year":"2019","venue":"SIG-GRAPH Asia 2019 Technical Briefs","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2017] Chengze Li, Xueting Liu, and Tien-Tsin Wong. Deep extraction of manga structural lines. ACM Trans. Graph., 36(4), 7 2017.","title":"Deep extraction of manga structural lines","authors":"Li","year":"2017","venue":"ACM Trans. Graph","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2021] Bing Li, Yuanlue Zhu, Yitong Wang, Chia-Wen Lin, Bernard Ghanem, and Linlin Shen. Anigan: Style-guided generative adversarial networks for unsupervised anime face gen- eration. IEEE Transactions on Multimedia, pages 1-1, 2021.","title":"Anigan: Style-guided generative adversarial networks for unsupervised anime face generation","authors":"Bing Li, Yuanlue Zhu, Yitong Wang, Chia-Wen Lin, Bernard Ghanem, Linlin Shen","year":"2021","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2019] Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, and Jan Kautz. Few-shot unsupervised image-to-image translation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10551-10560, 2019.","title":"Few-shot unsupervised image-to-image translation","authors":"Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, Jan Kautz","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Maejima et al., 2021] Akinobu Maejima, Hiroyuki Kubo, Seitaro Shinagawa, Takuya Funatomi, Tatsuo Yotsukura, Satoshi Naka- mura, and Yasuhiro Mukaigawa. Anime Character Colorization Using Few-Shot Learning. Association for Computing Machin- ery, New York, NY, USA, 2021.","title":"Anime Character Colorization Using Few-Shot Learning","authors":"Akinobu Maejima, Hiroyuki Kubo, Seitaro Shinagawa, Takuya Funatomi, Tatsuo Yotsukura, Satoshi Nakamura, Yasuhiro Mukaigawa","year":"2021","venue":"Association for Computing Machinery","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Masuda et al., 2019] Hiromichi Masuda, Tadashi Sudo, Kazuo Rikukawa, Yuji Mori, Naofumi Ito, Yasuo Kameyama, and Megumi Onouchi. Anime Industry Report. Technical report, The Association of Japanese Animations, 2019.","title":"Anime Industry Report","authors":"Hiromichi Masuda, Tadashi Sudo, Kazuo Rikukawa, Yuji Mori, Naofumi Ito, Yasuo Kameyama, Megumi Onouchi","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Mescheder et al., 2018] Lars Mescheder, Andreas Geiger, and Se- bastian Nowozin. Which training methods for gans do actually converge? In International conference on machine learning, pages 3481-3490. PMLR, 2018.","title":"Which training methods for gans do actually converge?","authors":"Mescheder","year":"2018","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Nir et al., 2022] Oron Nir, Gal Rapoport, and Ariel Shamir. CAST: Character labeling in Animation using Self-supervision by Track- ing. Computer Graphics Forum, 2022.","title":"CAST: Character labeling in Animation using Self-supervision by Tracking","authors":"Nir","year":"2022","venue":"Computer Graphics Forum","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Nizan and Tal, 2020] Ori Nizan and Ayellet Tal. Breaking the cy- cle -colleagues are all you need. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seat- tle, WA, USA, June 13-19, 2020, pages 7857-7866. Computer Vision Foundation / IEEE, 2020.","title":"Breaking the cycle -colleagues are all you need","authors":"Tal Nizan, Ori Nizan, Ayellet Tal","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ojha et al., 2021] Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang. Few- shot image generation via cross-domain correspondence. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10743-10752, 2021.","title":"Fewshot image generation via cross-domain correspondence","authors":"Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A Efros, Yong, Jae Lee, Eli Shechtman, Richard Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Pérez et al., 2003] Patrick Pérez, Michel Gangnet, and Andrew Blake. Poisson image editing. In ACM SIGGRAPH 2003 Pa- pers, pages 313-318. 2003.","title":"Poisson image editing","authors":"Patrick Pérez, Michel Gangnet, Andrew Blake","year":"2003","venue":"ACM SIGGRAPH 2003 Papers","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Reinhard et al., 2001] Erik Reinhard, Michael Adhikhmin, Bruce Gooch, and Peter Shirley. Color transfer between images. IEEE Computer graphics and applications, 21(5):34-41, 2001.","title":"Color transfer between images","authors":"Reinhard","year":"2001","venue":"IEEE Computer graphics and applications","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ren et al., 2015] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Proceedings of the 28th Interna- tional Conference on Neural Information Processing Systems - Volume 1, NIPS'15, page 91-99, Cambridge, MA, USA, 2015. MIT Press.","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","authors":"Ren","year":"2015","venue":"Proceedings of the 28th International Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Saito et al., 2020] Kuniaki Saito, Kate Saenko, and Ming-Yu Liu. COCO-FUNIT: few-shot unsupervised image translation with a content conditioned style encoder. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Com- puter Vision -ECCV 2020 -16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part III, volume 12348 of Lecture Notes in Computer Science, pages 382-398. Springer, 2020.","title":"COCO-FUNIT: few-shot unsupervised image translation with a content conditioned style encoder","authors":"Kuniaki Saito, Kate Saenko, Ming-Yu Liu","year":"2020","venue":"Computer Vision -ECCV 2020 -16th European Conference","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Schroff et al., 2015] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recog- nition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815-823, 2015.","title":"Facenet: A unified embedding for face recognition and clustering","authors":"Schroff","year":"2015","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Shimizu et al., 2021] Yugo Shimizu, Ryosuke Furuta, Delong Ouyang, Yukinobu Taniguchi, Ryota Hinami, and Shonosuke Ishiwatari. Painting style-aware manga colorization based on generative adversarial networks. In 2021 IEEE International Conference on Image Processing, ICIP 2021, Anchorage, AK, USA, September 19-22, 2021, pages 1739-1743. IEEE, 2021.","title":"Painting style-aware manga colorization based on generative adversarial networks","authors":"Yugo Shimizu, Ryosuke Furuta, Delong Ouyang, Yukinobu Taniguchi, Ryota Hinami, Shonosuke Ishiwatari","year":"2021","venue":"IEEE International Conference on Image Processing, ICIP 2021","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Siegel and Castellan, 1988] Sidney Siegel and N. John Castellan. Nonparametric Statistics for the Behavioral Sciences. McGrall- Hill International, 1988.","title":"Nonparametric Statistics for the Behavioral Sciences","authors":"Castellan Siegel, Sidney Siegel, N John Castellan","year":"1988","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Silva et al., 2019] Felipe Coelho Silva, Paulo André Lima de Cas- tro, Hélio Ricardo Júnior, and Ernesto Cordeiro Marujo. Mangan: Assisting colorization of manga characters concept art using con- ditional GAN. In 2019 IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, September 22-25, 2019, pages 3257-3261. IEEE, 2019.","title":"Mangan: Assisting colorization of manga characters concept art using conditional GAN","authors":"Felipe Coelho Silva, Paulo André Lima De Castro, Hélio, Ricardo Júnior, Ernesto Cordeiro Marujo","year":"2019","venue":"IEEE International Conference on Image Processing, ICIP 2019","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Simo-Serra et al., 2016] Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, and Hiroshi Ishikawa. Learning to simplify: Fully convolutional networks for rough sketch cleanup. ACM Trans. Graph., 35(4), 7 2016.","title":"Learning to simplify: Fully convolutional networks for rough sketch cleanup","authors":"Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, Hiroshi Ishikawa","year":"2016","venue":"ACM Trans. Graph","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Simo-Serra et al., 2018a] Edgar Simo-Serra, Satoshi Iizuka, and Hiroshi Ishikawa. Mastering sketching: Adversarial augmenta- tion for structured prediction. ACM Trans. Graph., 37(1), 1 2018.","title":"Mastering sketching: Adversarial augmentation for structured prediction","authors":"Simo-Serra","year":"2018","venue":"ACM Trans. Graph","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Simo-Serra et al., 2018b] Edgar Simo-Serra, Satoshi Iizuka, and Hiroshi Ishikawa. Real-time data-driven interactive rough sketch inking. ACM Trans. Graph., 37(4), 7 2018.","title":"Real-time data-driven interactive rough sketch inking","authors":"Simo-Serra","year":"2018","venue":"ACM Trans. Graph","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of ma- chine learning research, 9(11), 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yarbus, 1967] Alfred L. Yarbus. Eye Movements and Vision. Plenum Press, New York, 1967. Translated from Russian.","title":"Eye Movements and Vision","authors":"Alfred L Yarbus","year":"1967","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2018] Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, and Chunping Liu. Two-stage sketch colorization. ACM Trans. Graph., 37(6), 12 2018.","title":"Two-stage sketch colorization","authors":"Zhang","year":"2018","venue":"ACM Trans. Graph","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2020] Lvmin Zhang, Yi JI, and Chunping Liu. Dan- booregion: An illustration region dataset. In European Confer- ence on Computer Vision (ECCV), 2020.","title":"Danbooregion: An illustration region dataset","authors":"Zhang","year":"2020","venue":"European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}