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Position: Interactive Generative Video as Next-Generation Game Engine

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arxiv 2503.17359 v2 pith:HP7L3YPX submitted 2025-03-21 cs.CV

Jiwen Yu , Yiran Qin , Haoxuan Che , Quande Liu , Xintao Wang , Pengfei Wan , Di Zhang , Xihui Liu This is my paper · ORCID
classification cs.CV
keywords gamegenerativecontentinteractivevideodevelopmentenginesgeneration
verification ladder T0 review T1 audit T2 compute T3 formal

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Modern game development faces significant challenges in creativity and cost due to predetermined content in traditional game engines. Recent breakthroughs in video generation models, capable of synthesizing realistic and interactive virtual environments, present an opportunity to revolutionize game creation. In this position paper, we propose Interactive Generative Video (IGV) as the foundation for Generative Game Engines (GGE), enabling unlimited novel content generation in next-generation gaming. GGE leverages IGV's unique strengths in unlimited high-quality content synthesis, physics-aware world modeling, user-controlled interactivity, long-term memory capabilities, and causal reasoning. We present a comprehensive framework detailing GGE's core modules and a hierarchical maturity roadmap (L0-L4) to guide its evolution. Our work charts a new course for game development in the AI era, envisioning a future where AI-powered generative systems fundamentally reshape how games are created and experienced.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multiplayer Interactive World Models with Representation Autoencoders

    cs.CV 2026-07 accept novelty 7.0 of 10

    A 5B-parameter latent diffusion model generates real-time four-player Rocket League matches conditioned on all players' actions, staying stable far beyond its training horizon.

  2. Matrix-Game: Interactive World Foundation Model

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A 17B-parameter diffusion model generates controllable, physically consistent Minecraft video from a reference image and user actions, beating Oasis and MineWorld on a new benchmark.

  3. CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Causal Diffusion Policy adds historical action conditioning and attention cache sharing to diffusion-based robot policies, improving success rates on most tested manipulation tasks under degraded observations.

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

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Interactive world models are reorganized around the game-engine action-state-observation loop, and a 90-hour Black Myth: Wukong dataset with frame-aligned actions, ground-truth states, and observations is introduced.

  5. A Survey of Interactive Generative Video

    cs.CV 2025-04 conditional novelty 4.0 of 10

    A survey that divides interactive generative video research into five modules: generation, control, memory, dynamics, and intelligence.

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