Full metadata
Title
Applications of Machine Learning to Animation and Computer Graphics for Optimized Real-Time Performance
Description
This thesis surveys and analyzes applications of machine learning techniques to the fields of animation and computer graphics. Data-driven techniques utilizing machine learning have in recent years been successfully applied to many subfields of animation and computer graphics. These include, but are not limited to, fluid dynamics, kinematics, and character modeling. I argue that such applications offer significant advantages which will be pivotal in advancing the fields of animation and computer graphics. Further, I argue these advantages are especially relevant in real-time implementations when working with finite computational resources.
Date Created
2019-05
Contributors
- Saba, Raphael Lucas (Author)
- Foy, Joseph (Thesis director)
- Olson, Loren (Committee member)
- School of Mathematical and Statistical Sciences (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
40 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2018-2019
Handle
https://hdl.handle.net/2286/R.I.52510
Level of coding
minimal
Cataloging Standards
System Created
- 2019-04-16 12:06:35
System Modified
- 2021-08-11 04:09:57
- 3 years 3 months ago
Additional Formats