{"title":"从动画图形中快速分解精灵","authors":"Tomoyuki Suzuki, Kotaro Kikuchi, Kota Yamaguchi","doi":"arxiv-2408.03923","DOIUrl":null,"url":null,"abstract":"This paper presents an approach to decomposing animated graphics into\nsprites, a set of basic elements or layers. Our approach builds on the\noptimization of sprite parameters to fit the raster video. For efficiency, we\nassume static textures for sprites to reduce the search space while preventing\nartifacts using a texture prior model. To further speed up the optimization, we\nintroduce the initialization of the sprite parameters utilizing a pre-trained\nvideo object segmentation model and user input of single frame annotations. For\nour study, we construct the Crello Animation dataset from an online design\nservice and define quantitative metrics to measure the quality of the extracted\nsprites. Experiments show that our method significantly outperforms baselines\nfor similar decomposition tasks in terms of the quality/efficiency tradeoff.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"39 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast Sprite Decomposition from Animated Graphics\",\"authors\":\"Tomoyuki Suzuki, Kotaro Kikuchi, Kota Yamaguchi\",\"doi\":\"arxiv-2408.03923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an approach to decomposing animated graphics into\\nsprites, a set of basic elements or layers. Our approach builds on the\\noptimization of sprite parameters to fit the raster video. For efficiency, we\\nassume static textures for sprites to reduce the search space while preventing\\nartifacts using a texture prior model. To further speed up the optimization, we\\nintroduce the initialization of the sprite parameters utilizing a pre-trained\\nvideo object segmentation model and user input of single frame annotations. For\\nour study, we construct the Crello Animation dataset from an online design\\nservice and define quantitative metrics to measure the quality of the extracted\\nsprites. Experiments show that our method significantly outperforms baselines\\nfor similar decomposition tasks in terms of the quality/efficiency tradeoff.\",\"PeriodicalId\":501174,\"journal\":{\"name\":\"arXiv - CS - Graphics\",\"volume\":\"39 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Graphics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.03923\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.03923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents an approach to decomposing animated graphics into
sprites, a set of basic elements or layers. Our approach builds on the
optimization of sprite parameters to fit the raster video. For efficiency, we
assume static textures for sprites to reduce the search space while preventing
artifacts using a texture prior model. To further speed up the optimization, we
introduce the initialization of the sprite parameters utilizing a pre-trained
video object segmentation model and user input of single frame annotations. For
our study, we construct the Crello Animation dataset from an online design
service and define quantitative metrics to measure the quality of the extracted
sprites. Experiments show that our method significantly outperforms baselines
for similar decomposition tasks in terms of the quality/efficiency tradeoff.