Exploiting Novel Deep Learning Architecture in Character Animation Pipelines

Simple item page
dc.contributor.advisorTroje, Nikolaus
dc.contributor.authorGhorbani, Saeed
dc.date.accessioned2022-12-14T16:18:33Z
dc.date.available2022-12-14T16:18:33Z
dc.date.copyright2022-04-25
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:18:32Z
dc.degree.disciplineElectrical Engineering & Computer Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractThis doctoral dissertation aims to show a body of work proposed for improving different blocks in the character animation pipelines resulting in less manual work and more realistic character animation. To that purpose, we describe a variety of cutting-edge deep learning approaches that have been applied to the field of human motion modelling and character animation. The recent advances in motion capture systems and processing hardware have shifted from physics-based approaches to data-driven approaches that are heavily used in the current game production frameworks. However, despite these significant successes, there are still shortcomings to address. For example, the existing production pipelines contain processing steps such as marker labelling in the motion capture pipeline or annotating motion primitives, which should be done manually. In addition, most of the current approaches for character animation used in game production are limited by the amount of stored animation data resulting in many duplicates and repeated patterns. We present our work in four main chapters. We first present a large dataset of human motion called MoVi. Secondly, we show how machine learning approaches can be used to automate proprocessing data blocks of optical motion capture pipelines. Thirdly, we show how generative models can be used to generate batches of synthetic motion sequences given only weak control signals. Finally, we show how novel generative models can be applied to real-time character control in the game production.
dc.identifier.urihttp://hdl.handle.net/10315/40598
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subjectComputer science
dc.subject.keywordsMachine learning
dc.subject.keywordsComputer animation
dc.subject.keywordsComputer graphics
dc.titleExploiting Novel Deep Learning Architecture in Character Animation Pipelines
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ghorbani_Saeed_2022_Phd.pdf
Size:
30.78 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Electrical Engineering and Computer Science

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.