research-article

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

Authors:

Johanna Barbier,

Guillaume Gales,

Sebastian LutzAuthors Info & Claims

DigiPro '19: Proceedings of the 2019 Digital Production Symposium

Article No.: 6, Pages 1 - 5

https://doi.org/10.1145/3329715.3338880

Published: 27 July 2019 Publication History

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

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Cited By

Pal AMitra SLakshmi D(2024)Illuminating the Path From Script to Screen Using Lights, Camera, and AITransforming Cinema with Artificial Intelligence10.4018/979-8-3693-3916-9.ch006(97-142)Online publication date: 13-Dec-2024
https://doi.org/10.4018/979-8-3693-3916-9.ch006
Berrevoets JSeedat NImrie FVan Der Schaar MKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Differentiable and transportable structure learningProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618502(2206-2233)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618502
Trottnow JGreenly WShaw CHudson SHelzle VVera HRing D(2020)SAUCE: Asset Libraries of the FutureProceedings of the 2020 Digital Production Symposium10.1145/3403736.3403941(1-5)Online publication date: 11-Aug-2020
https://dl.acm.org/doi/10.1145/3403736.3403941

Index Terms

Jumping in at the deep end: how to experiment with machine learning in post-production software
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software prototyping

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Information & Contributors

Information

Published In

cover image ACM Conferences

DigiPro '19: Proceedings of the 2019 Digital Production Symposium

July 2019

52 pages

ISBN:9781450367998

DOI:10.1145/3329715

Conference Chairs:
Sandy Kao
DreamWorks Animation
,
Barbara Balents
BB Consulting
,
Program Chairs:
Trina Roy
Pixar Animation Studios
,
Rachel Rose
Industrial Light & Magic

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2019

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DigiPro '19: The Digital Production Symposium

July 27, 2019

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Cited By

Pal AMitra SLakshmi D(2024)Illuminating the Path From Script to Screen Using Lights, Camera, and AITransforming Cinema with Artificial Intelligence10.4018/979-8-3693-3916-9.ch006(97-142)Online publication date: 13-Dec-2024
https://doi.org/10.4018/979-8-3693-3916-9.ch006
Berrevoets JSeedat NImrie FVan Der Schaar MKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Differentiable and transportable structure learningProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618502(2206-2233)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618502
Trottnow JGreenly WShaw CHudson SHelzle VVera HRing D(2020)SAUCE: Asset Libraries of the FutureProceedings of the 2020 Digital Production Symposium10.1145/3403736.3403941(1-5)Online publication date: 11-Aug-2020
https://dl.acm.org/doi/10.1145/3403736.3403941

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