Review for NeurIPS paper: Variational Amodal Object Completion

This submission tackles the problem of amodal category-specific instance mask completion. To do this, they propose an interesting 3-stage training process for a variational autoencoder that maps partial masks to full masks, followed by resizing to match the object sizes. Reviewers were divided on whether the curriculum training process represents an important contribution; I think this is well-designed, but it could be more clearly motivated in the text. This is demonstrated both for the mask completion problem, and through combination with instance inpainters, for the instance completion problem in the RGB pixel space. During rebuttal experiments, authors also showed results (Tab 3, Fig 4) indicating that the method is able to produce diverse predictions in the occluded regions.