Summary of Diffusion Models Are Real-time Game Engines, by Dani Valevski et al.
Diffusion Models Are Real-Time Game Engines
by Dani Valevski, Yaniv Leviathan, Moab Arar, Shlomi Fruchter
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents GameNGen, a neural-powered game engine that enables real-time interaction with complex environments at high quality. The model can simulate classic games like DOOM at 20+ frames per second on a single TPU, achieving comparable PSNR to lossy JPEG compression. Human evaluators struggle to distinguish short clips of the simulated game from actual gameplay, indicating impressive fidelity. GameNGen’s training involves two phases: first, an RL-agent learns to play the game, and then a diffusion model is trained to predict the next frame based on past frames and actions. This conditioning enables stable generation over long trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GameNGen is a new way for computers to create games that feel real. It’s like having a super-smart artist who can keep making tiny changes to what they’re drawing, so it looks really good and doesn’t repeat itself. The program does this by learning how to play a game, then using that knowledge to predict what the next frame of the game should look like. This allows GameNGen to create games that are very detailed and interactive, which is important for people who want to enjoy games without getting bored or frustrated. |
Keywords
» Artificial intelligence » Diffusion model
