Authors:
(1) Li Siyao, S-Lab, Nanyang Technological University;
(2) Tianpei Gu, Lexica and Work completed at UCLA;
(3) Weiye Xiao, Southeast University;
(4) Henghui Ding, S-Lab, Nanyang Technological University;
(5) Ziwei Liu, S-Lab, Nanyang Technological University;
(6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Table of Links Abstract and 1. Introduction Related Work
Mixamo Line Art Dataset
Our Approach
Experiment
Conclusion and References 6. Conclusion In this study, we address the practical problem of cartoon line inbetweening and propose a novel approach that treats line arts as geometrized vector graphs. Unlike previous frame interpolation tasks on raster images, our approach formulates the inbetweening task as a graph fusion problem with vertex repositioning. We present a deep learning-based framework called AnimeInbet, which shows significant gains over existing methods in terms of both quantitative and qualitative evaluation. To facilitate training and evaluation on cartoon line inbetweening, we also provide a large-scale geometrized line art dataset, MixamoLine240. Our proposed framework and dataset facilitate a wide range of applications, such as anime production and multimedia design, and have significant practical implications. Acknowledgement. This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-PhD/2021-01- 031[T]). It is also supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This study is partially supported by NTU NAP, MOE AcRF Tier 1 (2021-T1-001-088). References [1] Mixamo. https://www.mixamo.com/. 3 [2] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396, 2003. 5 [3] Leonardo Carvalho, Ricardo Marroquim, and Emilio Vital Brazil. Dilight: Digital light table–inbetweening for 2d animations using guidelines. Computers & Graphics, 2017. 3 [4] Evan Casey, V´ıctor Perez, and Zhuoru Li. The animation ´ transformer: Visual correspondence via segment matching. In ICCV, 2021. 1, 3 [5] Shuhong Chen and Matthias Zwicker. Improving the perceptual quality of 2d animation interpolation. In ECCV, 2022. 1, 2, 6, 7 [6] Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time intermediate flow estimation for video frame interpolation. In ECCV, 2022. 1, 2, 6, 7 [7] Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz. Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. In CVPR, 2018. 2 [8] Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, and Jaegul Choo. Animeceleb: Largescale animation celebheads dataset for head reenactment. In ECCV, 2022. 3 [9] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2014. 7 [10] Johannes Kopf and Dani Lischinski. Digital reconstruction of halftoned color comics. ACM TOG, 31(6), 2012. 1, 3 [11] Xiaoyu Li, Bo Zhang, Jing Liao, and Pedro V. Sander. Deep sketch-guided cartoon video inbetweening. TVCG, 2020. 2 [12] Songtao Liu, Jin Huang, and Hao Zhang. End-to-end line drawing vectorization. In AAAI, 2022. 3, 4 [13] Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video frame synthesis using deep voxel flow. In CVPR, 2017. 2 [14] Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, and Jiaya Jia. Video frame interpolation with transformer. In CVPR, 2022. 1, 2, 6, 7 [15] Haoran Mo, Edgar Simo-Serra, Chengying Gao, Changqing Zou, and Ruomei Wang. General virtual sketching framework for vector line art. In SIGGRAPH, 2021. 3, 4, 7, 9 [16] Rei Narita, Keigo Hirakawa, and Kiyoharu Aizawa. Optical flow based line drawing frame interpolation using distance transform to support inbetweenings. In ICIP, 2019. 7 [17] Simon Niklaus and Feng Liu. Context-aware synthesis for video frame interpolation. In CVPR, 2018. 2 [18] Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. In CVPR, 2020. 2 [19] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive convolution. In CVPR, 2017. 2 [20] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive separable convolution. In ICCV, 2017. 2 [21] Junheum Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim. Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation. In ECCV, 2020. 2 [22] Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng. Manga colorization. ACM TOG, 25(3), 2006. 1, 3 [23] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In ECCV, 2022. 1, 2, 6, 7 [24] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In CVPR, 2020. 5 [25] Maria Shugrina, Ziheng Liang, Amlan Kar, Jiaman Li, Angad Singh, Karan Singh, and Sanja Fidler. Creative flow+ dataset. In CVPR, 2019. 3 [26] Hyeonjun Sim, Jihyong Oh, and Munchurl Kim. Xvfi: extreme video frame interpolation. In ICCV, 2021. 2 [27] Edgar Simo-Serra, Satoshi Iizuka, and Hiroshi Ishikawa. Mastering sketching: Adversarial augmentation for structured prediction. ACM TOG, 37(1), 2018. 2 [28] Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, and Hiroshi Ishikawa. Learning to simplify: Fully convolutional networks for rough sketch cleanup. ACM TOG, 35(4), 2016. 2 [29] Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, and Chen Change Loy. Animerun: 2d animation visual correspondence from open source 3d movies. In NeurIPS, 2022. 3 [30] Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, and Ziwei Liu. Deep animation video interpolation in the wild. In CVPR, 2021. 2 [31] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In CVPR, 2021. 5 [32] D. Sykora, J. Buri ` anek, and J. ´ Zˇ ara. Unsupervised coloriza- ´ tion of black-and-white cartoons. In Int. Symp. NPAR, 2004. 1, 3 [33] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In CVPR, 2022. 5 [34] Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, and MingHsuan Yang. Quadratic video interpolation. In NeurIPS, 2019. 2 [35] Wenwu Yang. Context-aware computer aided inbetweening. IEEE TVCG, 24(2):1049–1062, 2017. 3 [36] Chih-Yuan Yao, Shih-Hsuan Hung, Guo-Wei Li, I-Yu Chen, Reza Adhitya, and Yu-Chi Lai. Manga vectorization and manipulation with procedural simple screentone. IEEE TVCG, 23(2), 2016. 3, 4 [37] Lvmin Zhang and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023. 3 [38] Lvmin Zhang, Jinyue Jiang, Yi Ji, and Chunping Liu. Smartshadow: Artistic shadow drawing tool for line drawings. In ICCV, 2021. 1, 3 [39] Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, and Chunping Liu. Two-stage sketch colorization. In SIGGRAPH, 2018. 1, 3 [40] Song-Hai Zhang, Tao Chen, Yi-Fei Zhang, Shi-Min Hu, and Ralph R. Martin. Vectorizing cartoon animations. IEEE TVCG, 15(4), 2009. 3, 4 This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Li Siyao, S-Lab, Nanyang Technological University; (2) Tianpei Gu, Lexica and Work completed at UCLA; (3) Weiye Xiao, Southeast University; (4) Henghui Ding, S-Lab, Nanyang Technological University; (5) Ziwei Liu, S-Lab, Nanyang Technological University; (6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Authors: Authors: (1) Li Siyao, S-Lab, Nanyang Technological University; (2) Tianpei Gu, Lexica and Work completed at UCLA; (3) Weiye Xiao, Southeast University; (4) Henghui Ding, S-Lab, Nanyang Technological University; (5) Ziwei Liu, S-Lab, Nanyang Technological University; (6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work Mixamo Line Art Dataset Our Approach Experiment Conclusion and References Related Work Related Work Mixamo Line Art Dataset Mixamo Line Art Dataset Our Approach Our Approach Experiment Experiment Conclusion and References Conclusion and References 6. Conclusion In this study, we address the practical problem of cartoon line inbetweening and propose a novel approach that treats line arts as geometrized vector graphs. Unlike previous frame interpolation tasks on raster images, our approach formulates the inbetweening task as a graph fusion problem with vertex repositioning. We present a deep learning-based framework called AnimeInbet , which shows significant gains over existing methods in terms of both quantitative and qualitative evaluation. To facilitate training and evaluation on cartoon line inbetweening, we also provide a large-scale geometrized line art dataset, MixamoLine240 . Our proposed framework and dataset facilitate a wide range of applications, such as anime production and multimedia design, and have significant practical implications. AnimeInbet MixamoLine240 Acknowledgement . This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-PhD/2021-01- 031[T]). It is also supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This study is partially supported by NTU NAP, MOE AcRF Tier 1 (2021-T1-001-088). Acknowledgement References [1] Mixamo. https://www.mixamo.com/ . 3 https://www.mixamo.com/ [2] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396, 2003. 5 [3] Leonardo Carvalho, Ricardo Marroquim, and Emilio Vital Brazil. Dilight: Digital light table–inbetweening for 2d animations using guidelines. Computers & Graphics, 2017. 3 [4] Evan Casey, V´ıctor Perez, and Zhuoru Li. The animation ´ transformer: Visual correspondence via segment matching. In ICCV, 2021. 1, 3 [5] Shuhong Chen and Matthias Zwicker. Improving the perceptual quality of 2d animation interpolation. In ECCV, 2022. 1, 2, 6, 7 [6] Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time intermediate flow estimation for video frame interpolation. In ECCV, 2022. 1, 2, 6, 7 [7] Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz. Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. In CVPR, 2018. 2 [8] Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, and Jaegul Choo. Animeceleb: Largescale animation celebheads dataset for head reenactment. In ECCV, 2022. 3 [9] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2014. 7 [10] Johannes Kopf and Dani Lischinski. Digital reconstruction of halftoned color comics. ACM TOG, 31(6), 2012. 1, 3 [11] Xiaoyu Li, Bo Zhang, Jing Liao, and Pedro V. Sander. Deep sketch-guided cartoon video inbetweening. TVCG, 2020. 2 [12] Songtao Liu, Jin Huang, and Hao Zhang. End-to-end line drawing vectorization. In AAAI, 2022. 3, 4 [13] Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video frame synthesis using deep voxel flow. In CVPR, 2017. 2 [14] Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, and Jiaya Jia. Video frame interpolation with transformer. In CVPR, 2022. 1, 2, 6, 7 [15] Haoran Mo, Edgar Simo-Serra, Chengying Gao, Changqing Zou, and Ruomei Wang. General virtual sketching framework for vector line art. In SIGGRAPH, 2021. 3, 4, 7, 9 [16] Rei Narita, Keigo Hirakawa, and Kiyoharu Aizawa. Optical flow based line drawing frame interpolation using distance transform to support inbetweenings. In ICIP, 2019. 7 [17] Simon Niklaus and Feng Liu. Context-aware synthesis for video frame interpolation. In CVPR, 2018. 2 [18] Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. In CVPR, 2020. 2 [19] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive convolution. In CVPR, 2017. 2 [20] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive separable convolution. In ICCV, 2017. 2 [21] Junheum Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim. Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation. In ECCV, 2020. 2 [22] Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng. Manga colorization. ACM TOG, 25(3), 2006. 1, 3 [23] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In ECCV, 2022. 1, 2, 6, 7 [24] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In CVPR, 2020. 5 [25] Maria Shugrina, Ziheng Liang, Amlan Kar, Jiaman Li, Angad Singh, Karan Singh, and Sanja Fidler. Creative flow+ dataset. In CVPR, 2019. 3 [26] Hyeonjun Sim, Jihyong Oh, and Munchurl Kim. Xvfi: extreme video frame interpolation. In ICCV, 2021. 2 [27] Edgar Simo-Serra, Satoshi Iizuka, and Hiroshi Ishikawa. Mastering sketching: Adversarial augmentation for structured prediction. ACM TOG, 37(1), 2018. 2 [28] Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, and Hiroshi Ishikawa. Learning to simplify: Fully convolutional networks for rough sketch cleanup. ACM TOG, 35(4), 2016. 2 [29] Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, and Chen Change Loy. Animerun: 2d animation visual correspondence from open source 3d movies. In NeurIPS, 2022. 3 [30] Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, and Ziwei Liu. Deep animation video interpolation in the wild. In CVPR, 2021. 2 [31] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In CVPR, 2021. 5 [32] D. Sykora, J. Buri ` anek, and J. ´ Zˇ ara. Unsupervised coloriza- ´ tion of black-and-white cartoons. In Int. Symp. NPAR, 2004. 1, 3 [33] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In CVPR, 2022. 5 [34] Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, and MingHsuan Yang. Quadratic video interpolation. In NeurIPS, 2019. 2 [35] Wenwu Yang. Context-aware computer aided inbetweening. IEEE TVCG, 24(2):1049–1062, 2017. 3 [36] Chih-Yuan Yao, Shih-Hsuan Hung, Guo-Wei Li, I-Yu Chen, Reza Adhitya, and Yu-Chi Lai. Manga vectorization and manipulation with procedural simple screentone. IEEE TVCG, 23(2), 2016. 3, 4 [37] Lvmin Zhang and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023. 3 [38] Lvmin Zhang, Jinyue Jiang, Yi Ji, and Chunping Liu. Smartshadow: Artistic shadow drawing tool for line drawings. In ICCV, 2021. 1, 3 [39] Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, and Chunping Liu. Two-stage sketch colorization. In SIGGRAPH, 2018. 1, 3 [40] Song-Hai Zhang, Tao Chen, Yi-Fei Zhang, Shi-Min Hu, and Ralph R. Martin. Vectorizing cartoon animations. IEEE TVCG, 15(4), 2009. 3, 4 This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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Deep Geometrized Cartoon Line Inbetweening: Conclusion and References

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