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Playable Game Generation

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arxiv 2412.00887 v1 pith:J2IBPRAD submitted 2024-12-01 cs.AI

Mingyu Yang , Junyou Li , Zhongbin Fang , Sheng Chen , Yangbin Yu , Qiang Fu , Wei Yang , Deheng Ye This is my paper · ORCID
classification cs.AI
keywords gamegenerationmechanicsplayablereal-timeaccurategamesgenerated
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, Artificial Intelligence Generated Content (AIGC) has advanced from text-to-image generation to text-to-video and multimodal video synthesis. However, generating playable games presents significant challenges due to the stringent requirements for real-time interaction, high visual quality, and accurate simulation of game mechanics. Existing approaches often fall short, either lacking real-time capabilities or failing to accurately simulate interactive mechanics. To tackle the playability issue, we propose a novel method called \emph{PlayGen}, which encompasses game data generation, an autoregressive DiT-based diffusion model, and a comprehensive playability-based evaluation framework. Validated on well-known 2D and 3D games, PlayGen achieves real-time interaction, ensures sufficient visual quality, and provides accurate interactive mechanics simulation. Notably, these results are sustained even after over 1000 frames of gameplay on an NVIDIA RTX 2060 GPU. Our code is publicly available: https://github.com/GreatX3/Playable-Game-Generation. Our playable demo generated by AI is: http://124.156.151.207.

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Forward citations

Cited by 5 Pith papers

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    AniX generates controllable videos of a user-supplied character performing typed actions inside a user-supplied 3D scene by fine-tuning a pre-trained video generator on small locomotion datasets.

  2. Matrix-Game: Interactive World Foundation Model

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    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. Pre-Trained Video Generative Models as World Simulators

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    A lightweight action-conditioning module and a motion-reinforced loss convert pre-trained video generators into action-following world simulators that also speed up model-based reinforcement learning.

  4. Goku: Flow Based Video Generative Foundation Models

    cs.CV 2025-02 conditional novelty 5.0 of 10

    A joint image-video generation model family reports state-of-the-art benchmark scores using rectified flow transformers, with all key evidence self-reported and no artifacts released.

  5. AlayaWorld: Long-Horizon and Playable Video World Generation

    cs.CV 2026-07 conditional novelty 4.0 of 10

    AlayaWorld is a full-stack open-source framework for interactive video world generation, combining 3D spatial caching, error-bank training, and few-step distillation for real-time playable worlds.

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