{"title":"深度学习超级分辨率电影制作","authors":"Vaibhav Vavilala, Mark Meyer","doi":"10.1145/3388767.3407334","DOIUrl":null,"url":null,"abstract":"Upscaling techniques are commonly used to create high resolution images, which are cost-prohibitive or even impossible to produce otherwise. In recent years, deep learning methods have improved the detail and sharpness of upscaled images over traditional algorithms. Here we discuss the motivation and challenges of bringing deep learned super resolution to production at Pixar, where upscaling is useful for reducing render farm costs and delivering high resolution content.","PeriodicalId":368810,"journal":{"name":"Special Interest Group on Computer Graphics and Interactive Techniques Conference Talks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deep Learned Super Resolution for Feature Film Production\",\"authors\":\"Vaibhav Vavilala, Mark Meyer\",\"doi\":\"10.1145/3388767.3407334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Upscaling techniques are commonly used to create high resolution images, which are cost-prohibitive or even impossible to produce otherwise. In recent years, deep learning methods have improved the detail and sharpness of upscaled images over traditional algorithms. Here we discuss the motivation and challenges of bringing deep learned super resolution to production at Pixar, where upscaling is useful for reducing render farm costs and delivering high resolution content.\",\"PeriodicalId\":368810,\"journal\":{\"name\":\"Special Interest Group on Computer Graphics and Interactive Techniques Conference Talks\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Special Interest Group on Computer Graphics and Interactive Techniques Conference Talks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3388767.3407334\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Special Interest Group on Computer Graphics and Interactive Techniques Conference Talks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3388767.3407334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learned Super Resolution for Feature Film Production
Upscaling techniques are commonly used to create high resolution images, which are cost-prohibitive or even impossible to produce otherwise. In recent years, deep learning methods have improved the detail and sharpness of upscaled images over traditional algorithms. Here we discuss the motivation and challenges of bringing deep learned super resolution to production at Pixar, where upscaling is useful for reducing render farm costs and delivering high resolution content.