High Frame Rate Photorealistic Flame Rendering via Generative Adversarial Networks

Attia, M; Abobakr, A; Wei, L; Saleh, K; Iskander, J; Zhou, H; Nahavandi, D; Hossny, M; Nahavandi, S

High Frame Rate Photorealistic Flame Rendering via Generative Adversarial Networks

Attia, M Abobakr, A Wei, L Saleh, K Iskander, J Zhou, H Nahavandi, D Hossny, M Nahavandi, S

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 2391-2396
Issue Date:: 2019

Closed Access

	Filename	Description	Size
	08914043.pdf	Published version	288.5 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Attia, M
dc.contributor.author	Abobakr, A
dc.contributor.author	Wei, L
dc.contributor.author	Saleh, K
dc.contributor.author	Iskander, J
dc.contributor.author	Zhou, H
dc.contributor.author	Nahavandi, D
dc.contributor.author	Hossny, M
dc.contributor.author	Nahavandi, S
dc.date	2019-10-06
dc.date.accessioned	2020-06-02T22:38:05Z
dc.date.available	2020-06-02T22:38:05Z
dc.date.issued	2019
dc.identifier.citation	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 2391-2396
dc.identifier.isbn	978-1-7281-4569-3
dc.identifier.issn	2577-1655
dc.identifier.uri	http://hdl.handle.net/10453/141070
dc.description.abstract	In this paper we propose accelerating live rendering of flame using generative adversarial neural networks. The proposed method targets entertainment and simulation-based training industries whose demands for high fidelity and high frame rate increases steadily. The proposed approach takes image frames rendered with low voxel resolution (8 × 8 × 8 voxels at 90 FPS) and produces image frames equivalent to imagery produced from high voxel resolution (64 × 64 × 64 voxels) typically rendered at 3 FPS. The error was evaluated using the structural similarity image metric (SSIM). The average error between generated image frames and the ground truth recorded 92:7%±4:6%.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
dc.relation.ispartof	IEEE International Conference on Systems, Man and Cybernetics
dc.relation.isbasedon	10.1109/SMC.2019.8914043
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	High Frame Rate Photorealistic Flame Rendering via Generative Adversarial Networks
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
utslib.location.activity	Bari, Italy,
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-02T22:37:58Z
pubs.finish-date	2019-10-09
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-06
pubs.volume	2019-October
utslib.start-page	2391
dc.location	Piscataway, USA

Abstract:

In this paper we propose accelerating live rendering of flame using generative adversarial neural networks. The proposed method targets entertainment and simulation-based training industries whose demands for high fidelity and high frame rate increases steadily. The proposed approach takes image frames rendered with low voxel resolution (8 × 8 × 8 voxels at 90 FPS) and produces image frames equivalent to imagery produced from high voxel resolution (64 × 64 × 64 voxels) typically rendered at 3 FPS. The error was evaluated using the structural similarity image metric (SSIM). The average error between generated image frames and the ground truth recorded 92:7%±4:6%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141070