  Panoptic segmentation assigns semantic and instance ID labels to every pixel
of an image. As permutations of instance IDs are also valid solutions, the task
requires learning of high-dimensional one-to-many mapping. As a result,
state-of-the-art approaches use customized architectures and task-specific loss
functions. We formulate panoptic segmentation as a discrete data generation
problem, without relying on inductive bias of the task. A diffusion model is
proposed to model panoptic masks, with a simple architecture and generic loss
function. By simply adding past predictions as a conditioning signal, our
method is capable of modeling video (in a streaming setting) and thereby learns
to track object instances automatically. With extensive experiments, we
demonstrate that our simple approach can perform competitively to
state-of-the-art specialist methods in similar settings.
