Practical Machine Learning for Rendering: From Research to Deployment
Abstract
Applying machine learning to improve graphics rendering or asset pipelines is challenging. Practicalities such as proprietary datasets, network retraining, and deployment issues make it difficult to translate published research into deployed solutions. In this course, industry practitioners at the forefront of this interdisciplinary field discuss and outline potential solutions.
BibTeX
@inproceedings {10.2312:egt.20221059,
booktitle = {Eurographics 2022 - Tutorials},
editor = {Hahmann, Stefanie and Patow, Gustavo A.},
title = {{Practical Machine Learning for Rendering: From Research to Deployment}},
author = {Marshall, Carl S. and Vembar, Deepak S. and Ganguly, Sujoy and Guinier, Florent},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-172-4},
DOI = {10.2312/egt.20221059}
}
booktitle = {Eurographics 2022 - Tutorials},
editor = {Hahmann, Stefanie and Patow, Gustavo A.},
title = {{Practical Machine Learning for Rendering: From Research to Deployment}},
author = {Marshall, Carl S. and Vembar, Deepak S. and Ganguly, Sujoy and Guinier, Florent},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-172-4},
DOI = {10.2312/egt.20221059}
}
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Except where otherwise noted, this item's license is described as Attribution 4.0 International License