Abstract: We propose PartGAN, a novel generative model that disentangles and generates background, object shape, object texture, and decomposes objects into parts without any mask or part annotations. To achieve object-level disentanglement, we build upon prior work and maximize the mutual information between the generated factors and sampled latent prior codes. To achieve part-level decomposition, we learn a part generator, which decomposes an object into parts that are spatially localized and disjoint, and consistent across instances. Extensive experiments on multiple datasets demonstrate that PartGAN discovers consistent object parts, which enable part-based controllable image generation.