Deep Learning for Graphics

Mitra, Niloy J.; Ritschel, Tobias; Kokkinos, Iasonas; Guerrero, Paul; Kim, Vladimir; Rematas, Konstantinos; Yumer, Ersin

Deep Learning for Graphics

Files

013-015.pdf (111.17 KB)

Date

2018

Authors

Mitra, Niloy J.
Ritschel, Tobias
Kokkinos, Iasonas
Guerrero, Paul
Kim, Vladimir
Rematas, Konstantinos
Yumer, Ersin

Publisher

The Eurographics Association

Abstract

In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. More recently, other domains such as geometry processing, animation, video processing, and physical simulations have benefited from deep learning methods as well. The massive volume of research that has emerged in just a few years is often difficult to grasp for researchers new to this area. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.

        @inproceedings{10.2312:egt.20181029
,
booktitle = {EG 2018 - Tutorials
},
editor = {Ritschel, Tobias and Telea, Alexandru
},
title = {{Deep Learning for Graphics
}},
author = {Mitra, Niloy J. and 
Ritschel, Tobias and 
Kokkinos, Iasonas and 
Guerrero, Paul and 
Kim, Vladimir and 
Rematas, Konstantinos and 
Yumer, Ersin
},
year = {2018
},
publisher = {The Eurographics Association
},
ISSN = {1017-4656
},
ISBN = {},
DOI = {10.2312/egt.20181029
}
}