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| Attribute | Value |
|---|---|
| Model | OASIS |
| Lab / Organization | Decart |
| Category | Generative World Model |
| Subtype | Real-Time Interactive Simulator |
| World Model Type | Real-time playable world model |
| Primary Domain | Real-time game simulation |
| Architecture | Spatial autoencoder + latent diffusion transformer with real-time action conditioning |
| Modality | Actions → Video (real-time) |
| Training Method | Large-scale gameplay video training with action-conditioned diffusion |
| Status | active |
| Year | 2024 |
| Performance Index | 66/100 (medium confidence, v1.1) |
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OASIS demonstrates that a neural network can serve as an entire game engine, generating real-time interactive Minecraft-like environments at 20+ frames per second. Using a spatial autoencoder and a diffusion-based backbone, OASIS takes user inputs (keyboard/mouse) and generates the next frame in real-time, creating a fully playable world model without any traditional game engine.
OASIS is a real-time playable world model developed by Decart / Etched in 2024 for real-time game simulation.
Short extractable facts for answer engines and no-JS readers.
| Signal | Value |
|---|---|
| Definition | OASIS is a real-time playable world model developed by Decart / Etched in 2024 for real-time game simulation. |
| Short Description | An open-source real-time interactive world model that generates playable game environments at 20+ FPS entirely from a neural network. |
| Benchmark Rows | 1 |
| FAQ Entries | 1 |
| Related Models | 3 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-03-12 |
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Published benchmark evidence attached to this model record.
| Benchmark | Metric | Result | Source |
|---|---|---|---|
| Real-time Generation | FPS 20 FPS | 20+ FPS | Source |
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| Reference | Link |
|---|---|
| Decart, 2024. OASIS: A Universe in a Transformer. | Open source |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| Sora vs Genie 2 | Sora (OpenAI) vs Genie 2 (DeepMind) | Sora and Genie 2 both generate video from prompts, but they approach world simulation very differently. Sora generates passive, high-fidelity videos from text; Genie 2 generates interactive, controllable 3D environments from images. |
| OASIS vs GameNGen | OASIS (Decart) vs GameNGen (Google Research) | OASIS and GameNGen both demonstrate neural networks functioning as real-time game engines, but they target different games and use different architectures. They represent the emerging frontier of neural game engines. |
| OASIS vs DIAMOND | OASIS vs DIAMOND | Both use diffusion models as world models for interactive environments, but OASIS generates real-time playable Minecraft-like worlds while DIAMOND uses diffusion for model-based RL training in Atari. |
| Genie vs Genie 2 | Genie (v1) vs Genie 2 | Genie pioneered unsupervised interactive environment generation from video. Genie 2 massively scales this approach to generate persistent, interactive 3D worlds from single images. |
| Pandora vs OASIS | Pandora vs OASIS | Both generate interactive game-like worlds, but Pandora produces multi-domain video simulations with narrative control, while OASIS focuses on high-fidelity real-time open-world generation trained on Minecraft. |
| GameNGen vs DIAMOND | GameNGen vs DIAMOND | Both simulate game environments in real-time, but with radically different approaches: GameNGen uses a fine-tuned diffusion model for photorealistic DOOM simulation, while DIAMOND uses a diffusion-based world model for Atari with reinforcement learning. |
| IRIS vs DIAMOND | IRIS vs DIAMOND | Two approaches to learning game simulators: IRIS uses discrete tokenization with a GPT-like transformer, while DIAMOND leverages diffusion models for higher visual fidelity. |
| OASIS vs Pandora | OASIS vs Pandora | Two real-time neural game engines: OASIS generates Minecraft-like worlds at 20+ FPS using latent diffusion, while Pandora creates diverse game worlds using a hybrid autoregressive-diffusion architecture. |
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Not yet for production games, but it demonstrates the concept of a neural network acting as a complete game engine, generating environments in real-time from player input.
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Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
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