Overview
Field: Computer Vision (CV) Authors: Zhen Li, Zian Meng, Shuwei Shi, Mingliang Zhai, Jiaming Tan, Chuanhao Li, Kaipeng Zhang Published: 2026-07-15 arXiv: 2607.14076
English Abstract
Building interactive worlds that respond coherently to player actions has long been a shared goal of computer graphics, games, and artificial intelligence. Recent video generative models provide a data-driven route toward this goal by predicting future observations conditioned on user actions, and are increasingly regarded as potential next-generation game engines. Realizing a genuinely interactive game world, however, requires interaction outcomes that follow rules over evolving game conditions, consequences that persist over long horizons, and a generation loop that operates in real time. Conventional game engines realize these properties through a recurrent action-state-observation loop, in which player actions update an explicit game state according to predefined rules and observations are rendered from the resulting state. Using this loop as an organizing perspective, this paper surveys interactive game world modeling along four dimensions: player action control, game state dynamics, state-observation persistence, and real-time interactive generation. For each dimension, the authors start from the capabilities required by interactive game worlds, group existing approaches into representative families, and discuss the strengths and trade-offs of each family. As a complementary analysis, the paper proposes a scalable data engine for *Black Myth: Wukong* that collects over 90 hours of gameplay containing frame-aligned player actions, ground-truth game states, and visual observations, together with structural and semantic annotations, as a resource for state-aware game world modeling. The authors hope the survey offers a clear picture of the field and catalyzes further progress toward interactive game worlds.
*Auto-collected on 2026-07-17*