We introduce LumiNet, a novel architecture that leverages generative models and latent intrinsic representations for transferring lighting from one image to another. Given a source image and a target lighting image, LumiNet generates a relit version of the source scene that captures the target’s lighting. Our approach makes two key contributions: a data curation strategy from the StyleGAN-based relighting model for our training, and a modified diffusion-based Con-trolNet that processes both latent intrinsic properties from the source image and latent extrinsic properties from the target image. We further improve lighting transfer through a learned adaptor that injects the target’s latent extrinsic properties via cross-attention and light-weight fine-tuning.Unlike traditional ControlNet, which generates images with conditional maps from a single scene, LumiNet processes latent representations from two different images -preserving geometry and albedo from the source while transferring lighting characteristics from the target. Experiments demonstrate that our method successfully transfers complex lighting phenomena including specular highlights and indirect illumination across scenes with varying spatial layouts and materials, outperforming existing approaches on challenging indoor scenes using only images as input.

Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.