Episode

From Pixels to States: Rethinking Interactive World Models as Game Engines

Podcast
Daily Paper Cast
Published
Jul 18, 2026
Duration seconds
1076
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not_requested
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https://share.transistor.fm/s/66269037
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https://media.transistor.fm/66269037/9c5a2e4f.mp3
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/v1/public/podcasts/daily-paper-cast-7079649/episodes/from-pixels-to-states-rethinking-interactive-world-models-as-game-engines
Markdown
/podcast/daily-paper-cast-7079649/from-pixels-to-states-rethinking-interactive-world-models-as-game-engines.md

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Summary

šŸ¤— Upvotes: 23 | cs.CV Authors: Zhen Li, Zian Meng, Shuwei Shi, Mingliang Zhai, Jiaming Tan, Chuanhao Li, Kaipeng Zhang Title: From Pixels to States: Rethinking Interactive World Models as Game Engines Arxiv: http://arxiv.org/abs/2607.14076v1 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. Taking this loop as an organizing lens, this paper examines interactive game world modeling along four dimensions: player action control, game state dynamics, state-observation persistence, and real-time interactive generation. For each dimension, we start from the capabilities required by an interactive game world, group existing approaches into representative families, and discuss the strengths and trade-offs of each family. Complementing this analysis, we present a scalable data engine for Black Myth: Wukong that collects over 90 hours of gameplay with frame-aligned player actions, ground-truth game states, and visual observations, together with structured and semantic annotations, as a resource…