3D generative models of objects enable photorealistic image synthesis with 3Dcontrol. Existing methods model the scene as a global scene representation,ignoring the compositional aspect of the scene. Compositional reasoning canenable a wide variety of editing applications, in addition to enablinggeneralizable 3D reasoning. In this paper, we present a compositionalgenerative model, where each semantic part of the object is represented as anindependent 3D representation learned from only in-the-wild 2D data. We startwith a global generative model (GAN) and learn to decompose it into differentsemantic parts using supervision from 2D segmentation masks. We then learn tocomposite independently sampled parts in order to create coherent globalscenes. Different parts can be independently sampled while keeping the rest ofthe object fixed. We evaluate our method on a wide variety of objects and partsand demonstrate editing applications.<br
