AlayaWorld: Long-Horizon and Playable Video World Generation
Game worlds have traditionally been built through labor-intensive production pipelines, making them costly to develop, difficult to customization, and expensive to modify after deployment. Recent advances in video world models offer a fundamentally different paradigm. Rather than explicitly authoring every component of a virtual environment, these models autoregressively synthesize future observations conditioned on the current world state and user interactions, enabling playable worlds to be generated online. Trained on both gameplay recordings and real-world videos, they can capture diverse
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 58%leofan90/Awesome-World-Models →
- PossiblePossibly related (embedding) · 55%grandgaming9321-prog/reality-engine →
- PossiblePossibly related (embedding) · 50%xlang-ai/OSWorld →
- PossiblePossibly related (embedding) · 49%MIRA: Multiplayer Interactive World Models trained on Rocket League [R] →
- PossiblePossibly related (embedding) · 48%modelscope/FunClip →
- LinkedLinked via arxiv author · 85%Xuangeng Chu →
“AlayaWorld: Long-Horizon and Playable Video World Generation”
- LinkedLinked via arxiv author · 85%AlayaWorld Team →
“AlayaWorld: Long-Horizon and Playable Video World Generation”
- LinkedLinked via arxiv author · 85%Kaipeng Zhang →
“AlayaWorld: Long-Horizon and Playable Video World Generation”