TY  - GEN
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10081678
AV  - public
N2  - 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.
CY  - Los Angeles, California
A1  - Mitra, NJ
A1  - Kokkinos, I
A1  - Guerrero, P
A1  - Thuerey, N
A1  - Kim, V
A1  - Guibas, L
PB  - ACM
Y1  - 2019/07/28/
UR  - https://doi.org/10.1145/3305366.3328059
TI  - CreativeAI: Deep learning for graphics SIGGRAPH 2019
ER  -