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This paper introduces StatePlay, a state-aware game world model that integrates visual content generation with the prediction of game states to ensure mechanics-consistent gameplay. By employing a mixture-of-transformers architecture, StatePlay effectively maintains specialized representations for both visual and state data, allowing for cross-modal interactions that enhance the fidelity of generated game mechanics. Experimental results demonstrate that StatePlay significantly outperforms traditional models, achieving an 18.6% improvement in mechanics fidelity and maintaining a normalized L1 distance below 0.06 for state predictions.
Mechanics-consistent game generation is now achievable, with StatePlay improving fidelity by over 18% compared to traditional models.
Recent game world models can generate visually realistic and interactive environments conditioned on player actions. However, games are not defined by pixels alone; they are governed by explicit mechanics, namely state-dependent rules that control health reduction, skill activation, and game termination. These mechanics depend on precise internal states, such as health points, skill meters, and timers, which are tightly coupled with visual observations and determine how gameplay evolves. Without modeling these state dynamics, existing game world models may generate visually plausible rollouts but violate the underlying game rules. In this paper, we propose StatePlay, a novel state-aware game world model that jointly predicts visual content and game states to promote mechanics-consistent generation. StatePlay adopts a mixture-of-transformers (MoT)-style architecture that preserves specialized visual and state representations while enabling cross-modal interaction, allowing predicted states to guide frame generation. Each branch is further optimized with a distinct objective suited to its modality. Experiments show that StatePlay achieves an average normalized L1 distance below 0.06 for state prediction. Furthermore, compared with models without explicit state modeling, our method improves mechanics fidelity in generated game rollouts by 18.6%. Overall, our work highlights the importance of state-aware game world modeling and advances beyond pixel-level realism toward complete and mechanically faithful game generation.
