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 language | English |
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Title of host publication | ACM SIGGRAPH 2014 Courses, SIGGRAPH 2014 |
Publisher | Association for Computing Machinery |
ISBN (Print) | 9781450329620 |
DOIs | |
Publication status | Published - 2014 |
Event | ACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2014 - Vamcouver , Canada Duration: 10 Aug 2014 → 14 Aug 2014 |
Conference
Conference | ACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2014 |
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Country/Territory | Canada |
City | Vamcouver |
Period | 10/08/14 → 14/08/14 |