Image Compositing

Image compositing, the process of seamlessly integrating objects from different images, aims to create realistic and visually appealing composite images. Current research heavily utilizes deep learning, particularly diffusion models and transformers, to address challenges like object placement, lighting harmonization, and shadow/reflection generation, often employing techniques like adversarial learning and self-supervised training to improve realism. These advancements are improving the efficiency and quality of image editing workflows, with applications ranging from photo manipulation and video editing to data augmentation for computer vision tasks. The field is also exploring more robust and generalizable methods, focusing on handling diverse scenarios and improving the accuracy of object segmentation and placement.