%0 Generic
%A Mitra, NJ
%A Kokkinos, I
%A Guerrero, P
%A Thuerey, N
%A Kim, V
%A Guibas, L
%C Los Angeles, California
%D 2019
%F discovery:10081678
%I ACM
%T CreativeAI: Deep learning for graphics SIGGRAPH 2019
%U https://discovery.ucl.ac.uk/id/eprint/10081678/
%X 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.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.