Diffusion Models Are Real-Time Game Engines
Authors: name: Dani Valevski, name: Yaniv Leviathan, name: Moab Arar, name: Shlomi Fruchter

We present GameNGen, the first game engine powered entirely by a neural model
that enables real-time interaction with a complex environment over long
trajectories at high quality. GameNGen can interactively simulate the classic
game DOOM at over 20 frames per second on a single TPU. Next frame prediction
achieves a PSNR of 29.4, comparable to lossy JPEG compression. Human raters are
only slightly better than random chance at distinguishing short clips of the
game from clips of the simulation. GameNGen is trained in two phases: (1) an
RL-agent learns to play the game and the training sessions are recorded, and
(2) a diffusion model is trained to produce the next frame, conditioned on the
sequence of past frames and actions. Conditioning augmentations enable stable
auto-regressive generation over long trajectories.