Diffusion Models Are Real-Time Game Engines
On the map: World models → Video-generation world models and simulators
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Abstract (arXiv)
We present GameNGen, the first game engine powered entirely by a neural model that also enables real-time interaction with a complex environment over long trajectories at high quality. When trained on the classic game DOOM, GameNGen extracts gameplay and uses it to generate a playable environment that can interactively simulate new trajectories. GameNGen runs at 20 frames per second on a single TPU and remains stable over extended multi-minute play sessions. 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, even after 5 minutes of auto-regressive generation. 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 help ensure stable auto-regressive generation over long trajectories, and decoder fine-tuning improves the fidelity of visual details and text.
Key facts. Cited by 282 papers in the map (data through 7 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 29 of its 40 references that are in the map. First posted on arXiv 27 Aug 2024.
Builds on
29 of 40 references.
Closest ancestors · 3
Same direction, newest first
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
- 2023Learning Interactive Real-World Simulators9 Oct
- 2021VideoGPT: Video Generation using VQ-VAE and Transformers20 Apr
Other references · 26
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
- 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
- 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
14 more
- 2018World Modelscited by 1,444
- 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
- 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
- 2023One-step Diffusion with Distribution Matching Distillationcited by 978
- 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 896
- 2024Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusioncited by 634
- 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
- 2023Photorealistic Video Generation with Diffusion Modelscited by 304
- 2024Rolling Diffusion Modelscited by 102
- 2020Learning to Simulate Dynamic Environments with GameGANcited by 86
- 2021Playable Video Generationcited by 63
- 2018ViZDoom Competitions: Playing Doom from Pixelscited by 50
- 2023Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Modelscited by 22
- 2018Comparison between CS and JPEG in terms of image compressioncited by 1
Built on by
Citing papers per year: 2024: 19; 2025: 119; 2026: 144.
282 papers.
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