Jumping in at the deep end: how to experiment with machine learning in post-production software

D. Ring, J. Barbier, Guillaume Gales, Ben Kent, Sebastian Lutz
{"title":"Jumping in at the deep end: how to experiment with machine learning in post-production software","authors":"D. Ring, J. Barbier, Guillaume Gales, Ben Kent, Sebastian Lutz","doi":"10.1145/3329715.3338880","DOIUrl":null,"url":null,"abstract":"Recent years has seen an explosion in Machine Learning (ML) research. The challenge is now to transfer these new algorithms into the hands of artists and TD's in visual effects and animation studios, so that they can start experimenting with ML within their existing pipelines. This paper presents some of the current challenges to experimentation and deployment of ML frameworks in the post-production industry. It introduces our open-source \"ML-Server\" client / server system as an answer to enabling rapid prototyping, experimentation and development of ML models in post-production software. Data, code and examples for the system can be found on the GitHub repository page: https://github.com/TheFoundryVisionmongers/nuke-ML-server","PeriodicalId":365444,"journal":{"name":"Proceedings of the 2019 Digital Production Symposium","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 Digital Production Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3329715.3338880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Recent years has seen an explosion in Machine Learning (ML) research. The challenge is now to transfer these new algorithms into the hands of artists and TD's in visual effects and animation studios, so that they can start experimenting with ML within their existing pipelines. This paper presents some of the current challenges to experimentation and deployment of ML frameworks in the post-production industry. It introduces our open-source "ML-Server" client / server system as an answer to enabling rapid prototyping, experimentation and development of ML models in post-production software. Data, code and examples for the system can be found on the GitHub repository page: https://github.com/TheFoundryVisionmongers/nuke-ML-server
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
跳入深渊:如何在后期制作软件中实验机器学习
近年来,机器学习(ML)研究呈爆炸式增长。现在的挑战是将这些新算法转移到视觉效果和动画工作室的艺术家和TD手中,这样他们就可以开始在现有的管道中试验ML。本文介绍了目前在后期制作行业中实验和部署ML框架所面临的一些挑战。它介绍了我们的开源“ML- server”客户端/服务器系统,作为在后期制作软件中实现快速原型,实验和ML模型开发的答案。系统的数据、代码和示例可以在GitHub存储库页面上找到:https://github.com/TheFoundryVisionmongers/nuke-ML-server
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
SpLit Physically based lens flare rendering in "The Lego Movie 2" Jumping in at the deep end: how to experiment with machine learning in post-production software Millefiori: a USD-based sequence editor Distributed multi-context interactive rendering
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1