Ioannou, E. and Maddock, S. orcid.org/0000-0003-3179-0263 (Accepted: 2023) Neural style transfer for computer games. In: The 34th British Machine Vision Conference workshop on Computer Vision for Games and Games for Computer Vision (CVG). 34th British Machine Vision workshop on Computer Vision for Games and Games for Computer Vision (CVG), 20-24 Nov 2023, Aberdeen, UK. British Machine Vision Association
Abstract
Neural Style Transfer (NST) research has been applied to images, videos, 3D meshes and radiance fields, but its application to 3D computer games remains relatively unexplored. Whilst image and video NST systems can be used as a post-processing effect for a computer game, this results in undesired artefacts and diminished post-processing effects. Here, we present an approach for injecting depth-aware NST as part of the 3D rendering pipeline. Qualitative and quantitative experiments are used to validate our in-game stylisation framework. We demonstrate temporally consistent results of artistically stylised game scenes, outperforming state-of-the-art image and video NST methods.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 The Author(s). |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Dec 2023 15:10 |
Last Modified: | 15 Dec 2023 15:20 |
Status: | Published |
Publisher: | British Machine Vision Association |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206654 |
Download
Filename: NST_in_Computer_Games__BMVC_2023_.pdf
