Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training
Video games provide a scalable source of training data for video world models, offering diverse environments, complex interactions, and abundant in-the-wild gameplay videos. However, raw gameplay footage entangles the game world with screen-space interfaces, introducing game-specific biases and irrelevant dynamics that hinder world-model training. To address this problem, we introduce GameUI-Taxonomy and G2WEngine, a full-stack framework that formalizes gameplay UI grounding and removal. G2WEngine automatically extracts reusable UI assets from real gameplay videos and synthesizes temporally co
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- PossiblePossibly related (embedding) · 49%Flux 3 X Mimic: The Next Generation of Video-Action Models →
- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- LinkedLinked via arxiv author · 85%Wenxuan Shen →
“Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training”
- LinkedLinked via arxiv author · 85%Dongna Jin →
“Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training”
- LinkedLinked via arxiv author · 85%Dongping Chen →
“Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training”