%J ACM SIGGRAPH 2019 Courses, SIGGRAPH 2019
%C Los Angeles, California
%A NJ Mitra
%A I Kokkinos
%A P Guerrero
%A N Thuerey
%A V Kim
%A L Guibas
%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.
%B Proceeding SIGGRAPH '19 ACM
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%T CreativeAI: Deep learning for graphics SIGGRAPH 2019
%L discovery10081678
%I ACM
%D 2019