  We present LM-GAN, an HDR sky model that generates photorealistic environment
maps with weathered skies. Our sky model retains the flexibility of traditional
parametric models and enables the reproduction of photorealistic all-weather
skies with visual diversity in cloud formations. This is achieved with flexible
and intuitive user controls for parameters, including sun position, sky color,
and atmospheric turbidity.
  Our method is trained directly from inputs fitted to real HDR skies, learning
both to preserve the input's illumination and correlate it to the real
reference's atmospheric components in an end-to-end manner. Our main
contributions are a generative model trained on both sky appearance and scene
rendering losses, as well as a novel sky-parameter fitting algorithm. We
demonstrate that our fitting algorithm surpasses existing approaches in both
accuracy and sky fidelity, and also provide quantitative and qualitative
analyses, demonstrating LM-GAN's ability to match parametric input to
photorealistic all-weather skies. The generated HDR environment maps are ready
to use in 3D rendering engines and can be applied to a wide range of
image-based lighting applications.
