GameFactory: Creating New Games with Generative Interactive Videos

• URL: http://arxiv.org/abs/2501.08325v1
• Date: Tue, 14 Jan 2025 18:57:21 GMT
• Title: GameFactory: Creating New Games with Generative Interactive Videos
• Authors: Jiwen Yu, Yiran Qin, Xintao Wang, Pengfei Wan, Di Zhang, Xihui Liu,
• Abstract summary: We present GameFactory, a framework focused on exploring scene generalization in game video generation. We propose a multi-phase training strategy that decouples game style learning from action control, preserving open-domain generalization. We extend our framework to enable autoregressive action-controllable game video generation, allowing the production of unlimited-length interactive game videos.
• Score: 32.98135338530966
• License:
• Abstract: Generative game engines have the potential to revolutionize game development by autonomously creating new content and reducing manual workload. However, existing video-based game generation methods fail to address the critical challenge of scene generalization, limiting their applicability to existing games with fixed styles and scenes. In this paper, we present GameFactory, a framework focused on exploring scene generalization in game video generation. To enable the creation of entirely new and diverse games, we leverage pre-trained video diffusion models trained on open-domain video data. To bridge the domain gap between open-domain priors and small-scale game dataset, we propose a multi-phase training strategy that decouples game style learning from action control, preserving open-domain generalization while achieving action controllability. Using Minecraft as our data source, we release GF-Minecraft, a high-quality and diversity action-annotated video dataset for research. Furthermore, we extend our framework to enable autoregressive action-controllable game video generation, allowing the production of unlimited-length interactive game videos. Experimental results demonstrate that GameFactory effectively generates open-domain, diverse, and action-controllable game videos, representing a significant step forward in AI-driven game generation. Our dataset and project page are publicly available at \url{https://vvictoryuki.github.io/gamefactory/}.

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