Scene Inference for Object Illumination Editing

The seamless illumination integration between a foreground object and a background scene is an important but challenging task in computer vision and augmented reality community. However, to our knowledge, there is no publicly available high-quality dataset that meets the illumination seamless integration task, which greatly hinders the development of this research direction. To this end, we apply a physically-based rendering method to create a large-scale, high-quality dataset, named IH dataset, which provides rich illumination information for seamless illumination integration task. In addition, we propose a deep learning-based SI-GAN method, a multi-task collaborative network, which makes full use of the multi-scale attention mechanism and adversarial learning strategy to directly infer mapping relationship between the inserted foreground object and corresponding background environment, and edit object illumination according to the proposed illumination exchange mechanism in parallel network. By this means, we can achieve the seamless illumination integration without explicit estimation of 3D geometric information. Comprehensive experiments on both our dataset and real-world images collected from the Internet show that our proposed SI-GAN provides a practical and effective solution for image-based object illumination editing, and validate the superiority of our method against state-of-the-art methods.