Because of the diversity in lighting environments, existing illumination estimation techniques have been designed explicitly on indoor or outdoor environments. Methods have focused specifically on capturing accurate energy (e.g., through parametric lighting models), which emphasizes shading and strong cast shadows; or producing plausible texture (e.g., with GANs), which prioritizes plausible reflections. Approaches which provide editable lighting capabilities have been proposed, but these tend to be with simplified lighting models, offering limited realism. In this work, we propose to bridge the gap between these recent trends in the literature, and propose a method which combines a parametric light model with 360{\deg} panoramas, ready to use as HDRI in rendering engines. We leverage recent advances in GAN-based LDR panorama extrapolation from a regular image, which we extend to HDR using parametric spherical gaussians. To achieve this, we introduce a novel lighting co-modulation method that injects lighting-related features throughout the generator, tightly coupling the original or edited scene illumination within the panorama generation process. In our representation, users can easily edit light direction, intensity, number, etc. to impact shading while providing rich, complex reflections while seamlessly blending with the edits. Furthermore, our method encompasses indoor and outdoor environments, demonstrating state-of-the-art results even when compared to domain-specific methods.

翻译：由于光照环境的多样性，现有光照估计技术通常针对室内或室外场景分别设计。这些方法要么专注于通过参数化光照模型精确捕捉能量分布，从而强调明暗与强烈投射阴影；要么通过生成对抗网络生成逼真的纹理，优先保证反射效果的可信度。虽然已有研究提出支持可编辑光照功能的方法，但大多采用简化光照模型，可控性与真实感有限。为此，本文致力于弥合当前文献中两种技术路线间的鸿沟，提出一种将参数化光照模型与360度全景图相结合的方法，可直接作为高动态范围图像用于渲染引擎。我们借鉴了近期基于生成对抗网络从普通图像外推低动态范围全景图的成果，并通过参数化球面高斯函数将其扩展至高动态范围。为实现这一目标，我们提出一种全新的光照协同调制方法，在生成器各层级注入光照相关特征，使全景图生成过程与原始或编辑后的场景光照紧密耦合。在该表示框架下，用户可轻松编辑光照方向、强度、数量等参数，在影响明暗表现的同时，生成丰富而复杂的反射效果，并实现编辑操作的无缝融合。此外，我们的方法同时适用于室内外环境，即使与针对特定领域的专用方法相比，仍展现出最优结果。