Summary of Playable Game Generation, by Mingyu Yang et al.
Playable Game Generation
by Mingyu Yang, Junyou Li, Zhongbin Fang, Sheng Chen, Yangbin Yu, Qiang Fu, Wei Yang, Deheng Ye
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel method called PlayGen has been proposed to tackle the challenges of generating playable games that meet real-time interaction, high visual quality, and accurate simulation of game mechanics. This approach 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. The results are sustained even after over 1000 frames of gameplay on an NVIDIA RTX 2060 GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Playable games generated by AI have become a reality! Researchers have developed a new method called PlayGen to create playable games that can be played in real-time, with high-quality visuals and accurate game mechanics. This is a big deal because previous attempts at generating playable games fell short. With PlayGen, you can play games for over 1000 frames without any issues. The results are amazing! |
Keywords
» Artificial intelligence » Autoregressive » Diffusion model
