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Deep synthesis of cloud lighting
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Satilmis, Pinar, Marnerides, Demetris, Debattista, Kurt and Bashford-Rogers, Thomas (2022) Deep synthesis of cloud lighting. IEEE Computer Graphics and Applications, 42 (5). pp. 8-18. doi:10.1109/MCG.2022.3172846 ISSN 0272-1716.
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Official URL: https://doi.org/10.1109/MCG.2022.3172846
Abstract
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 cloudscapes to be rapidly synthesized, and used for lighting virtual environments.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Rendering (Computer graphics), Deep learning (Machine learning), Image processing -- Digital techniques | ||||||||
| Journal or Publication Title: | IEEE Computer Graphics and Applications | ||||||||
| Publisher: | Institute of Electrical and Electronics Engineers | ||||||||
| ISSN: | 0272-1716 | ||||||||
| Official Date: | 1 September 2022 | ||||||||
| Dates: |
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| Volume: | 42 | ||||||||
| Number: | 5 | ||||||||
| Page Range: | pp. 8-18 | ||||||||
| DOI: | 10.1109/MCG.2022.3172846 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 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. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Description: | Free access |
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| Date of first compliant deposit: | 7 November 2022 | ||||||||
| Date of first compliant Open Access: | 7 November 2022 | ||||||||
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