Playable Game Generation

TL;DR

This paper introduces PlayGen, a novel AI-based method for generating playable games that ensures real-time interaction, high visual quality, and accurate mechanics simulation, validated on popular 2D and 3D games.

Contribution

The paper presents PlayGen, a comprehensive framework combining game data generation, autoregressive DiT-based diffusion, and playability evaluation for AI-generated games.

Findings

Achieves real-time game generation on standard GPU hardware.

Maintains visual quality and mechanics accuracy over 1000 frames.

Validated on well-known 2D and 3D games.

Abstract

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.