StatePlay：狀態感知遊戲世界模型實現機制一致性生成

摘要

近年来的游戏世界模型能够根据玩家动作生成视觉逼真且可互动的环境。然而，游戏并非仅由像素定义，而是受制于明确的机制，即控制生命值减少、技能激活及游戏终止的状态依赖规则。这些机制依赖于精确的内部状态（如生命值、技能条和计时器），这些状态与视觉观察紧密耦合，并决定游戏玩法的演进过程。若未能建模此类状态动态，现有游戏世界模型可能生成视觉上看似合理、但违反底层游戏规则的展开内容。本文提出StatePlay——一种新型状态感知游戏世界模型，该模型联合预测视觉内容与游戏状态，以促进符合机制的生成。StatePlay采用混合变换器（MoT）架构，在保持专用视觉与状态表征的同时实现跨模态交互，使预测状态能够引导帧生成。每个分支进一步针对其模态特性优化了独立目标函数。实验表明，StatePlay在状态预测上实现了平均归一化L1距离低于0.06。此外，与未显式建模状态的模型相比，我们的方法在游戏展开生成中的机制保真度提升了18.6%。总体而言，本研究凸显了状态感知游戏世界建模的重要性，并推动从像素级真实感迈向完整且机制忠实的游戏生成。

English

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.