深度学习超级分辨率电影制作

Vaibhav Vavilala, Mark Meyer
{"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}
引用次数: 1

摘要

升级技术通常用于创建高分辨率图像,这是成本过高,甚至不可能产生。近年来,与传统算法相比,深度学习方法改善了升级图像的细节和清晰度。在这里,我们讨论了将深度学习超分辨率引入皮克斯生产的动机和挑战,其中升级对于降低渲染农场成本和提供高分辨率内容是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信