Introduction to Machine Learning for Computer Graphics

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Computer Graphics is increasingly using techniques from Machine Learning. The trend is motivated by several factors, but the difficulties and expense of modelling is a major driving force. Here 'modelling' is used very broadly to include models of reflection (learn the BRDF of a real material), animation (learn the motion of real objects), as well as three-dimensional models (learn to model complex things). Building around a few examples, we will explore the whys and hows of Machine Learning within Computer Graphics. The course will outline the basics of data-driven modelling, introduce the foundations of probability and statistics, describe some useful distributions, and differentiate between ML and MAP problems. The ideas are illustrated using examples drawn from previous SIGGRAPHs; we'll help non-artists to draw, animate traffic flow from sensor data, and model moving trees from video. 2014 Copyright held by the Owner/Author.
Original languageEnglish
Title of host publicationACM SIGGRAPH 2014 Courses, SIGGRAPH 2014
PublisherAssociation for Computing Machinery
ISBN (Print)9781450329620
DOIs
Publication statusPublished - 2014
EventACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2014 - Vamcouver , Canada
Duration: 10 Aug 201414 Aug 2014

Conference

ConferenceACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2014
CountryCanada
CityVamcouver
Period10/08/1414/08/14

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    Hall, P. M. (2014). Introduction to Machine Learning for Computer Graphics. In ACM SIGGRAPH 2014 Courses, SIGGRAPH 2014 [20] Association for Computing Machinery. https://doi.org/10.1145/2614028.2615461