Genie: Generative Interactive Environments
Jake Bruce Michael Dennis Ashley Edwards Jack Parker-Holder Yuge Shi Edward Hughes Matthew Lai Aditi Mavalankar Richie Steigerwald Chris Apps Yusuf Aytar Sarah Bechtle Feryal Behbahani Stephanie Chan Nicolas Heess Lucy Gonzalez Simon Osindero Sherjil Ozair Scott Reed Jingwei Zhang Konrad Zolna Jeff Clune Nando de Freitas Satinder Singh Tim Rocktäschel
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.