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Commun. Appl."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            Changing the style of an image\/video while preserving its content is a crucial criterion to access a new neural style transfer algorithm. However, it is very challenging to transfer a new map art style to a certain video in which \u201ccontent\u201d comprises a map background and animation objects. In this article, we present a novel comprehensive system that solves the problems in transferring map art style in such video. Our system takes as input an arbitrary video, a map image, and an off-the-shelf map art image. It then generates an artistic video without damaging the functionality of the map and the consistency in details. To solve this challenge, we propose a novel network,\n            <jats:italic>Map Art Video Network<\/jats:italic>\n            (MAViNet), the tailored objective functions, and a rich training set with rich animation contents and different map structures. We have evaluated our method on various challenging cases and many comparisons with those of the related works. Our method substantially outperforms state-of-the-art methods in terms of visual quality and meets the mentioned criteria in this research domain.\n          <\/jats:p>","DOI":"10.1145\/3572030","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T11:54:56Z","timestamp":1669204496000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Structure-aware Video Style Transfer with Map Art"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9667-9780","authenticated-orcid":false,"given":"Thi-Ngoc-Hanh","family":"Le","sequence":"first","affiliation":[{"name":"National Cheng-Kung University, Tainan, Taiwan, Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1780-0066","authenticated-orcid":false,"given":"Ya-Hsuan","family":"Chen","sequence":"additional","affiliation":[{"name":"National Cheng-Kung University, Tainan, Taiwan, Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6699-2944","authenticated-orcid":false,"given":"Tong-Yee","family":"Lee","sequence":"additional","affiliation":[{"name":"National Cheng-Kung University, Tainan, Taiwan, Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"862","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"An Jie","year":"2021","unstructured":"Jie An, Siyu Huang, Yibing Song, Dejing Dou, Wei Liu, and Jiebo Luo. 2021. 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