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| Attribute | Value |
|---|---|
| Model | Lyra 2.0 |
| Lab / Organization | NVIDIA |
| Category | Generative World Model |
| Subtype | Autoregressive Interactive World Model |
| World Model Type | Autoregressive Interactive World Model |
| Primary Domain | Interactive Video |
| Architecture | Four-step autoregressive camera-conditioned world model |
| Modality | Image + Camera Poses -> Interactive Video |
| Training Method | Autoregressive interactive world modeling |
| Status | emerging |
| Year | 2026 |
Main editorial body preserved directly in static HTML.
Lyra 2.0 generates an evolving environment conditioned on camera input. It is included as a native interactive system rather than a text-to-video model wrapped in WBench's chained-context protocol.
Lyra 2.0 is an autoregressive interactive world model developed by NVIDIA in 2026 for interactive video.
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| Signal | Value |
|---|---|
| Definition | Lyra 2.0 is an autoregressive interactive world model developed by NVIDIA in 2026 for interactive video. |
| Short Description | Lyra 2.0 is a native camera-conditioned interactive world model evaluated by WBench. |
| Benchmark Rows | 1 |
| FAQ Entries | 1 |
| Related Models | 2 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-08-31 |
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Published benchmark evidence attached to this model record.
| Benchmark | Metric | Result | Source |
|---|---|---|---|
| WBench | Five-dimension average 76.4 /100 | 76.4 | Source |
Primary references preserved in static HTML for citation extraction.
| Reference | Link |
|---|---|
| NVIDIA. Lyra 2.0. Primary project source. | Open source |
| Ying et al., 2026. WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation. | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| SANA-WM | Generative World Model | Minute-Scale Interactive World Model | N/A |
| NVIDIA Cosmos | Foundation World Model | Video world foundation model | 87/100 |
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Lyra 2.0 exposes native camera conditioning and generates an evolving interactive environment; WBench evaluates that interface directly.
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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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