Position: Interactive Generative Video as Next-Generation Game Engine
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909626816004096 |
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| author | Yu, Jiwen Qin, Yiran Che, Haoxuan Liu, Quande Wang, Xintao Wan, Pengfei Zhang, Di Liu, Xihui |
| author_facet | Yu, Jiwen Qin, Yiran Che, Haoxuan Liu, Quande Wang, Xintao Wan, Pengfei Zhang, Di Liu, Xihui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position: Interactive Generative Video as Next-Generation Game Engine Yu, Jiwen Qin, Yiran Che, Haoxuan Liu, Quande Wang, Xintao Wan, Pengfei Zhang, Di Liu, Xihui Computer Vision and Pattern Recognition 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. |
| title | Position: Interactive Generative Video as Next-Generation Game Engine |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.17359 |