Title: Generative Hierarchical Models for Parts, Objects, and Scenes. Abstract: Hierarchical structure such as part-whole relationship in objects and scenes are the most inherent structure in natural scenes. Learning such representation via unsupervised learning can provide various benefits such as interpretability, compositionality, and transferability, which are important in many downstream tasks. In this paper, we propose the first hierarchical generative model for learning multiple latent part-whole relationships in a scene. During inference, taking top-down approach, our model infers the representation of more abstract concept (e.g., objects) and then infers that of more specific concepts (e.g., parts) by conditioning on the corresponding abstract concept. This makes the model avoid a difficult problem of routing between parts and whole. In experiments on images containing multiple objects with different shapes and part compositions, we demonstrate that our model can learn the latent hierarchical structure between parts and wholes and generate imaginary scenes.