Compositing multiple objects from different sources into one image is a formidable computer graphics challenge involving relative scaling, spatial layout, occlusion, and viewpoint transformation of objects from the 3D world to a 2D image. Now the task can be accomplished by an AI-powered Compositional Generative Adversarial Network (GAN). A new paper from a team of UC Berkeley researchers addresses the compositionality problem with no explicit prior information provided regarding the layout of objects. The key to this method is to fix one object while resizing and transforming other objects according to their complex joint interactions. These are learned by AI during a process of individual object combination and separation in the image.