[Submitted on 14 Oct 2026]
Title:RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay
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Abstract:Generative gameplay requires more than immediate responses to player this http URL 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. We introduce \rig (Reciprocally Interactive Generation), a framework for reciprocal interaction in generative gameplay. \rig 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. 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. Project page: this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.20811 [cs.CV]
(or arXiv:2610.20811v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.20811
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mengfei Li [view email] [v1] Wed, 14 Oct 2026 18:57:47 UTC (40,483 KB)
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