跳入深渊:如何在后期制作软件中实验机器学习

D. Ring, J. Barbier, Guillaume Gales, Ben Kent, Sebastian Lutz
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引用次数: 2

摘要

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