Jumping in at the Deep End: How to Experiment with Machine Learning in Post-Production Software

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-productionsoftware. Data, code and examples for the system can be found on the GitHub repository page:https://github.com/TheFoundryVisionmongers/nuke-ML-server

CONCEPTS

Computing methodologies→Machine learning;Software and its engineering→Software prototyping;
KEYWORDS deep learning, machine learning, visual computing, image processing, computer vision, deployment, integration, frameworks

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https://doi.org/10.1145/3329715.3338880CCS

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