{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:08:13Z","timestamp":1777655293249,"version":"3.51.4"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972162"],"award-info":[{"award-number":["61972162"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong International Science and Technology Cooperation Project","award":["2021A0505030009"],"award-info":[{"award-number":["2021A0505030009"]}]},{"name":"Guangzhou Basic and Applied Research Project","award":["202102021074"],"award-info":[{"award-number":["202102021074"]}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Natural Science Foundation","doi-asserted-by":"crossref","award":["2021A1515012625"],"award-info":[{"award-number":["2021A1515012625"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]},{"name":"CCF-Tencent Open Research fund","award":["CCF-Tencent RAGR20210114"],"award-info":[{"award-number":["CCF-Tencent RAGR20210114"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p>Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the pixelization process into specific cell-aware and aliasing-aware stages, mitigating the ambiguities in joint learning of cell size, aliasing effect, and color assignment. To train our model, we construct a dedicated pixel art dataset and augment it with different cell sizes and different degrees of anti-aliasing effects. Extensive experiments demonstrate its superior performance over state-of-the-arts in terms of cell sharpness and perceptual expressiveness. We also show promising results of video game pixelization for the first time. Code and dataset are available at https:\/\/github.com\/WuZongWei6\/Pixelization.<\/jats:p>","DOI":"10.1145\/3550454.3555482","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T21:19:07Z","timestamp":1669843147000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Make Your Own Sprites"],"prefix":"10.1145","volume":"41","author":[{"given":"Zongwei","family":"Wu","sequence":"first","affiliation":[{"name":"South China University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangyu","family":"Chai","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nanxuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Bath, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailin","family":"Deng","sequence":"additional","affiliation":[{"name":"Cardiff University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongtuo","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Netherlands and South China University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Wen","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China and South China University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junle","family":"Wang","sequence":"additional","affiliation":[{"name":"Tencent, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengfeng","family":"He","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Miko\u0142aj Bi\u0144kowski Danica J Sutherland Michael Arbel and Arthur Gretton. 2018. Demystifying MMD GANs. In ICLR."},{"key":"e_1_2_2_2_1","volume-title":"Imagenet: A large-scale hierarchical image database. In CVPR. 248--255.","author":"Deng Jia","year":"2009","unstructured":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In CVPR. 248--255."},{"key":"e_1_2_2_3_1","unstructured":"Ruigang Fu Qingyong Hu Xiaohu Dong Yulan Guo Yinghui Gao and Biao Li. 2020. Axiom-based grad-cam: Towards accurate visualization and explanation of cnns. In BMVC."},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2012.12.007"},{"key":"e_1_2_2_5_1","unstructured":"Timothy Gerstner Doug DeCarlo Marc Alexa Adam Finkelstein Yotam I Gingold and Andrew Nealen. 2012. Pixelated image abstraction. In NPAR@ Expressive. 29--36."},{"key":"e_1_2_2_6_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3272127.3275082","article-title":"Deep unsupervised pixelization","volume":"37","author":"Han Chu","year":"2018","unstructured":"Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, and Tien-Tsin Wong. 2018. Deep unsupervised pixelization. ACM TOG 37, 6 (2018), 1--11.","journal-title":"ACM TOG"},{"key":"e_1_2_2_7_1","volume-title":"NeurIPS","volume":"30","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, Vol. 30."},{"key":"e_1_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Xun Huang and Serge Belongie. 2017. Arbitrary style transfer in real-time with adaptive instance normalization. In ICCV. 1501--1510.","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Tiffany Inglis and Craig S Kaplan. 2012. Pixelating vector line art. In SIGGRAPH Posters. 108.","DOI":"10.1145\/2342896.2343021"},{"key":"e_1_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Tiffany Inglis Daniel Vogel and Craig S Kaplan. 2013. Rasterizing and antialiasing vector line art in the pixel art style. In NPAR. 25--32.","DOI":"10.1145\/2486042.2486044"},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Phillip Isola Jun-Yan Zhu Tinghui Zhou and Alexei A Efros. 2017. Image-to-image translation with conditional adversarial networks. In CVPR. 1125--1134.","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020. Analyzing and improving the image quality of stylegan. In CVPR. 8110--8119.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_2_2_15_1","volume-title":"Adam: A method for stochastic optimization. In ICLR.","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR."},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964921.1964994"},{"key":"e_1_2_2_17_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2508363.2508370","article-title":"Content-adaptive image down-scaling","volume":"32","author":"Kopf Johannes","year":"2013","unstructured":"Johannes Kopf, Ariel Shamir, and Pieter Peers. 2013. Content-adaptive image down-scaling. ACM TOG 32, 6 (2013), 1--8.","journal-title":"ACM TOG"},{"key":"e_1_2_2_18_1","volume-title":"Electronic and Automation Control Conference (IMCEC)","volume":"4","author":"Kuang Hailan","year":"2021","unstructured":"Hailan Kuang, Nan Huang, Shuchang Xu, and Shunpeng Du. 2021. A Pixel image generation algorithm based on CycleGAN. In 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Vol. 4. IEEE, 476--480."},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3151666.3151708"},{"key":"e_1_2_2_20_1","first-page":"1076","article-title":"L0-Regularized Image Down-scaling","volume":"27","author":"Liu Junjie","year":"2018","unstructured":"Junjie Liu, Shengfeng He, and Rynson W. H. Lau. 2018. L0-Regularized Image Down-scaling. IEEE TIP 27, 3 (2018), 1076--1085.","journal-title":"IEEE TIP"},{"key":"e_1_2_2_21_1","volume-title":"Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)."},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766891"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"e_1_2_2_24_1","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, Vol. 32. 8026--8037.","journal-title":"NeurIPS"},{"key":"e_1_2_2_25_1","volume-title":"Xgan: Unsupervised image-to-image translation for many-to-many mappings. In Domain Adaptation for Visual Understanding","author":"Royer Am\u00e9lie","year":"2020","unstructured":"Am\u00e9lie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, and Kevin Murphy. 2020. Xgan: Unsupervised image-to-image translation for many-to-many mappings. In Domain Adaptation for Visual Understanding. Springer, 33--49."},{"key":"e_1_2_2_26_1","unstructured":"Sato. 2020. PixelMe: Convert your photo into pixelart. https:\/\/pixel-me.tokyo\/en\/."},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2021.01.008"},{"key":"e_1_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Ashish Shrivastava Tomas Pfister Oncel Tuzel Joshua Susskind Wenda Wang and Russell Webb. 2017. Learning from simulated and unsupervised images through adversarial training. In CVPR. 2107--2116.","DOI":"10.1109\/CVPR.2017.241"},{"key":"e_1_2_2_29_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In ICLR."},{"key":"e_1_2_2_30_1","unstructured":"Yaniv Taigman Adam Polyak and Lior Wolf. 2017. Unsupervised cross-domain image generation. In ICLR."},{"key":"e_1_2_2_31_1","volume-title":"Filters for common resampling tasks. Graphics Gems I","author":"Turkowski Ken","year":"1990","unstructured":"Ken Turkowski. 1990. Filters for common resampling tasks. Graphics Gems I (1990), 147--165."},{"key":"e_1_2_2_32_1","volume-title":"Cosface: Large margin cosine loss for deep face recognition. In CVPR. 5265--5274.","author":"Wang Hao","year":"2018","unstructured":"Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu. 2018. Cosface: Large margin cosine loss for deep face recognition. In CVPR. 5265--5274."},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980239"},{"key":"e_1_2_2_34_1","unstructured":"Jun-Yan Zhu Taesung Park Phillip Isola and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICCV. 2223--2232."},{"key":"e_1_2_2_35_1","doi-asserted-by":"crossref","unstructured":"C Lawrence Zitnick and Devi Parikh. 2013. Bringing semantics into focus using visual abstraction. In CVPR. 3009--3016.","DOI":"10.1109\/CVPR.2013.387"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3550454.3555482","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3550454.3555482","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:43Z","timestamp":1750182703000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3550454.3555482"}},"subtitle":["Aliasing-Aware and Cell-Controllable Pixelization"],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":35,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["10.1145\/3550454.3555482"],"URL":"https:\/\/doi.org\/10.1145\/3550454.3555482","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,30]]},"assertion":[{"value":"2022-11-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}