In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In an increasing variety of problem settings, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.
@inproceedings{Mitra:2018:CDL:3277644.3277775, author = {Mitra, Niloy J. and Kokkinos, Iasonas and Guerrero, Paul and Thuerey, Nils and Ritschel, Tobias}, title = {CreativeAI: Deep Learning for Graphics}, booktitle = {SIGGRAPH Asia 2018 Courses}, series = {SA ’18}, year = {2018}, isbn = {978-1-4503-6026-5}, location = {Tokyo, Japan}, pages = {4:1--4:249}, articleno = {4}, numpages = {249}, url = {http://doi.acm.org/10.1145/3277644.3277775}, doi = {10.1145/3277644.3277775}, acmid = {3277775}, }