Deep Synthesis of Cloud Lighting

Satilmis, Pinar and Marnerides, Demetris and Debattista, Kurt and Bashford-Rogers, Thomas (2022) Deep Synthesis of Cloud Lighting. IEEE Computer Graphics and Applications. ISSN 0272-1716

CGA-2021-08-0080.R2_Satilmis_with_authors.pdf - Accepted Version

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Current appearance models for the sky are able to represent clear sky illumination to a high degree of accuracy. However, these models all lack a common feature of real-skies: clouds. These are an essential component for many applications which rely on realistic skies, such as image editing and synthesis. While clouds can be added to existing sky models through rendering, this is hard to achieve due to the difficulties of representing clouds and the complexities of volumetric light transport. In this work, an alternative approach to this problem is proposed whereby clouds are synthesized using a learned data-driven representation. This leverages a captured collection of High Dynamic Range cloudy sky imagery, and combines this dataset with clear sky models to produce plausible cloud appearance from a coarse representation of cloud positions. This representation is artist controllable, allowing for novel cloud scapes to be rapidly synthesized, and used for lighting virtual environments.

Item Type: Article
Identification Number:
2 May 2022Accepted
5 May 2022Published Online
Uncontrolled Keywords: Cloud computing; Clouds; Lighting; Atmospheric modeling; Computational modeling , Rendering (computer graphics) , Neural networks
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
CAH11 - computing > CAH11-01 - computing > CAH11-01-06 - computer games and animation
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Pinar Satilmis
Date Deposited: 07 Jun 2022 10:00
Last Modified: 22 Mar 2023 12:00

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