We present SILT, a Self-supervised Implicit Lighting Transfer method. Unlike previous research on scene relighting, we do not seek to apply arbitrary new lighting configurations to a given scene. Instead, we wish to transfer the lighting style from a database of other scenes, to provide a uniform lighting style regardless of the input. The solution operates as a two-branch network that first aims to map input images of any arbitrary lighting style to a unified domain, with extra guidance achieved through implicit image decomposition. We then remap this unified input domain using a discriminator that is presented with the generated outputs and the style reference, i.e. images of the desired illumination conditions. Our method is shown to outperform supervised relighting solutions across two different datasets without requiring lighting supervision.

翻译：我们展示了SSILT, 这是一种自我监督的隐性照明传输方法。 与以往的现场点火研究不同, 我们并不寻求将任意的新的照明配置应用到特定场景。 相反, 我们希望将照明风格从其他场景的数据库中转移, 以提供统一的照明风格, 不论输入内容如何。 解决方案作为一个双管网络运行, 首先将任意照明风格的图像输入到一个统一的域, 并通过隐性图像分解实现额外的指导 。 然后, 我们使用一个带有生成输出和风格引用的区分器重新绘制这个统一的输入域, 即想要的照明条件的图像。 我们的方法显示在不需要照明监督的情况下, 超过两个不同的数据集的受监督的光源化解决方案 。