SkyGAN: Towards Realistic Cloud Imagery for Image Based Lighting
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Achieving photorealism when rendering virtual scenes in movies or architecture visualizations often depends on providing a realistic illumination and background. Typically, spherical environment maps serve both as a natural light source from the Sun and the sky, and as a background with clouds and a horizon. In practice, the input is either a static high-resolution HDR photograph manually captured on location in real conditions, or an analytical clear sky model that is dynamic, but cannot model clouds. Our approach bridges these two limited paradigms: a user can control the sun position and cloud coverage ratio, and generate a realistically looking environment map for these conditions. It is a hybrid data-driven analytical model based on a modified state-of-the-art GAN architecture, which is trained on matching pairs of physically-accurate clear sky radiance and HDR fisheye photographs of clouds. We demonstrate our results on renders of outdoor scenes under varying time, date, and cloud covers.
Description
CCS Concepts: Computing methodologies --> Rendering; Supervised learning; Applied computing --> Earth and atmospheric sciences
@inproceedings{10.2312:sr.20221151,
booktitle = {Eurographics Symposium on Rendering},
editor = {Ghosh, Abhijeet and Wei, Li-Yi},
title = {{SkyGAN: Towards Realistic Cloud Imagery for Image Based Lighting}},
author = {Mirbauer, Martin and Rittig, Tobias and Iser, Tomáš and Krivánek, Jaroslav and Šikudová, Elena},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1727-3463},
ISBN = {978-3-03868-187-8},
DOI = {10.2312/sr.20221151}
}