In this work, we present MuDI, a novel framework that enables multi-subject personalization by effectively decoupling identities from multiple subjects.

We then propose to employ layout, a very abstract and easy-to-obtain prior, as the spatial guidance for subject arrangement.

World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner.