  We introduce Genie, the first generative interactive environment trained in
an unsupervised manner from unlabelled Internet videos. The model can be
prompted to generate an endless variety of action-controllable virtual worlds
described through text, synthetic images, photographs, and even sketches. At
11B parameters, Genie can be considered a foundation world model. It is
comprised of a spatiotemporal video tokenizer, an autoregressive dynamics
model, and a simple and scalable latent action model. Genie enables users to
act in the generated environments on a frame-by-frame basis despite training
without any ground-truth action labels or other domain-specific requirements
typically found in the world model literature. Further the resulting learned
latent action space facilitates training agents to imitate behaviors from
unseen videos, opening the path for training generalist agents of the future.
