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.

翻译：我们提出了LM-GAN，这是一种能够生成带有气象特征的高动态范围天空环境图的高动态范围天空模型。该模型保留了传统参数化模型的灵活性，同时能够重现具有视觉多样性的照相级全天气天空效果。通过提供灵活直观的参数控制（包括太阳位置、天空颜色和大气浑浊度），实现了这一功能。我们的方法直接基于真实高动态范围天空数据的输入进行训练，以端到端方式学习保留输入光照信息，并将其与真实参考图像的天气成分相关联。主要贡献包括：基于天空外观与场景渲染损失联合训练的生成模型，以及一种新型天空参数拟合算法。实验证明，该拟合算法在精度和天空真实度方面均超越现有方法，并通过定量与定性分析展示了LM-GAN将参数化输入匹配至照相级全天气天空的能力。生成的高动态范围环境图可直接用于三维渲染引擎，并适用于多种基于图像的光照应用。