Mitra, NJ;
Kokkinos, I;
Guerrero, P;
Thuerey, N;
Kim, V;
Guibas, L;
(2019)
CreativeAI: Deep learning for graphics SIGGRAPH 2019.
In:
Proceeding SIGGRAPH '19 ACM.
ACM: Los Angeles, California.
Preview |
Text
part1_introduction_niloy.pdf - Accepted Version Download (17MB) | Preview |
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, often 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, often requiring application-specific learning architectures. 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 course gives an organized overview of core theory, practice, and graphics-related applications of deep learning.
| Type: | Proceedings paper |
|---|---|
| Title: | CreativeAI: Deep learning for graphics SIGGRAPH 2019 |
| Event: | SIGGRAPH '19 ACM |
| ISBN-13: | 9781450363075 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3305366.3328059 |
| Publisher version: | https://doi.org/10.1145/3305366.3328059 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10081678 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
