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| Attribute | Value |
|---|---|
| Model | GAIA-2 |
| Lab / Organization | Wayve |
| Category | Generative World Model |
| Subtype | Controllable Multi-View Driving World Model |
| World Model Type | Controllable multi-view generative world model for driving |
| Primary Domain | Autonomous Driving |
| Architecture | Controllable multi-view generative world model |
| Modality | Multi-View Driving Video + Structured Conditions → Future Driving Video |
| Training Method | Generative video world modeling with structured conditioning |
| Status | active |
| Year | 2025 |
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GAIA-2 is Wayve's second-generation driving world model. Its technical report explicitly defines it as a controllable multi-view generative world model, with rich conditioning for scenario generation and evaluation.
GAIA-2 is a controllable multi-view generative world model for driving developed by Wayve in 2025 for autonomous driving.
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| Signal | Value |
|---|---|
| Definition | GAIA-2 is a controllable multi-view generative world model for driving developed by Wayve in 2025 for autonomous driving. |
| Short Description | Wayve's controllable multi-view world model for generating driving scenarios from structured conditioning signals. |
| Benchmark Rows | 0 |
| FAQ Entries | 1 |
| Related Models | 3 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-07-19 |
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| Reference | Link |
|---|---|
| Wayve, 2025. GAIA-2: A Controllable Multi-View World Model. | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| GAIA-1 | Foundation World Model | Generative driving world model | 61/100 |
| Waabi World | Generative World Model | Generative world simulator for autonomous driving | N/A |
| NVIDIA Cosmos | Foundation World Model | Video world foundation model | 87/100 |
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Wayve's technical report explicitly describes GAIA-2 as a controllable multi-view generative world model.
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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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Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-07-19.
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