StatePlay: 状态感知的游戏世界模型用于机制一致生成

摘要

近期开发的游戏世界模型能够根据玩家的操作生成视觉逼真且可交互的环境。然而，游戏并非仅由像素定义，而是受显式机制支配——即控制生命值减少、技能激活和游戏终止等状态依赖规则。这些机制依赖于精确的内部状态（如生命值、技能槽和计时器），这些状态与视觉观测紧密耦合，决定了游戏进程的演变方式。若不对这些状态动态进行建模，现有游戏世界模型可能生成视觉上合理但违背底层游戏规则的推演序列。为此，本文提出StatePlay——一种新颖的状态感知游戏世界模型，通过联合预测视觉内容与游戏状态，促进符合机制约束的生成过程。StatePlay采用混合Transformer架构，在保留专用视觉与状态表征的同时实现跨模态交互，使预测的状态能够指导帧生成。每个分支根据其模态特性优化特定目标函数。实验表明，StatePlay在状态预测任务中平均归一化L1距离低于0.06。此外，与未显式建模状态的模型相比，本方法将生成游戏推演序列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，超越了像素级真实感的局限，推动了完整且机制忠实的游戏生成技术发展。

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.