RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay

Computer Science > Computer Vision and Pattern Recognition arXiv:2610.20811 (cs) [Submitted on 14 Oct 2026] Title: RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay Authors: Mengfei Li, Yi Zhang, Xingtong Ge, Lunjie Zhu, Fangyu Lin, Peng Li, Guanglu Song, Yu Liu, Wei Xue, Wenhan Luo, Yike Guo.

计算机科学 > 计算机视觉与模式识别 arXiv:2610.20811 (cs) [提交于 2026 年 10 月 14 日] 标题：RIGen：生成式游戏玩法中超越动作跟随的交互式生成。作者：Mengfei Li, Yi Zhang, Xingtong Ge, Lunjie Zhu, Fangyu Lin, Peng Li, Guanglu Song, Yu Liu, Wei Xue, Wenhan Luo, Yike Guo。

Abstract: Generative gameplay requires more than immediate responses to player actions. It should have persistent consequences, while the generated world should act according to its own state and rules, shaping the player’s situation and subsequent choices. Video generation captures local dynamics, but limited history, partially observed game state, and the need for contextual behavior decisions make such interactions difficult to sustain through audiovisual prediction alone.

摘要：生成式游戏玩法需要的不仅仅是对玩家动作的即时响应。它应该具有持久的后果，同时生成的游戏世界应根据其自身的状态和规则运行，从而塑造玩家的处境和后续选择。视频生成可以捕捉局部动态，但由于历史记录有限、游戏状态部分可见，以及对情境行为决策的需求，仅通过视听预测很难维持此类交互。

We introduce RIGen (Reciprocally Interactive Generation), a framework for reciprocal interaction in generative gameplay. RIGen combines persistent state and configurable rules, contextual interaction reasoning, and action-conditioned audiovisual generation. Together, these components make interaction consequences and actor behavior jointly determine generated content while maintaining consistency between game state and visible outcomes.

我们引入了 RIGen（交互式生成），这是一个用于生成式游戏玩法中相互交互的框架。RIGen 结合了持久状态和可配置规则、情境交互推理以及动作条件下的视听生成。这些组件共同作用，使交互后果和参与者行为能够共同决定生成的内容，同时保持游戏状态与可见结果之间的一致性。

We construct a Xonotic gameplay dataset to train an audiovisual generator for learning dynamics and interactions. Further post-training enables RIGarena, an interactive generative first-person shooter (FPS) prototype. Experiments demonstrate more reliable combat outcomes and reciprocal interaction between the player and the generated world.

我们构建了一个 Xonotic 游戏数据集，用于训练视听生成器以学习动态和交互。进一步的训练促成了 RIGarena，这是一个交互式生成第一人称射击 (FPS) 原型。实验证明了更可靠的战斗结果以及玩家与生成世界之间的相互交互。