  Neural video game simulators emerged as powerful tools to generate and edit
videos. Their idea is to represent games as the evolution of an environment's
state driven by the actions of its agents. While such a paradigm enables users
to play a game action-by-action, its rigidity precludes more semantic forms of
control. To overcome this limitation, we augment game models with prompts
specified as a set of natural language actions and desired states. The result-a
Promptable Game Model (PGM)-makes it possible for a user to play the game by
prompting it with high- and low-level action sequences. Most captivatingly, our
PGM unlocks the director's mode, where the game is played by specifying goals
for the agents in the form of a prompt. This requires learning "game AI",
encapsulated by our animation model, to navigate the scene using high-level
constraints, play against an adversary, and devise a strategy to win a point.
To render the resulting state, we use a compositional NeRF representation
encapsulated in our synthesis model. To foster future research, we present
newly collected, annotated and calibrated Tennis and Minecraft datasets. Our
method significantly outperforms existing neural video game simulators in terms
of rendering quality and unlocks applications beyond the capabilities of the
current state of the art. Our framework, data, and models are available at
https://snap-research.github.io/promptable-game-models/.
