用文字指令跨游戏融合关卡，实现可控生成。
Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation
- 通过共享潜在空间对齐文本与关卡结构。
- 跨游戏关卡融合质量显著提升，支持零样本组合生成。
- 适合游戏设计、生成式AI研究者使用。
文本到关卡生成旨在将自然语言描述转化为结构化游戏关卡，实现对程序化内容生成的直观控制。现有方法通常局限于单一游戏领域，而将语言条件生成扩展至多游戏需学习捕捉跨领域结构关系的表示。我们提出 Multiverse，一个基于语言的多游戏关卡生成器，可通过文本指令实现跨游戏关卡融合。该模型学习一个共享潜在空间，对齐文本指令与关卡结构，并采用基于阈值的多正例对比监督，连接语义相关的跨游戏关卡。这一表示使语言可指导在融合不同游戏内容时保留哪些结构特征，支持通过潜在空间插值和组合文本提示进行零样本生成。实验表明，所学表示支持可控的跨游戏关卡融合，在同类型游戏内融合质量显著提升，并为语言条件下的多游戏内容生成提供统一表示。
原文摘要 · Abstract (English)
Text-to-level generation aims to translate natural language descriptions into structured game levels, enabling intuitive control over procedural content generation. While prior text-to-level generators are typically limited to a single game domain, extending language-conditioned generation to multiple games requires learning representations that capture structural relationships across domains. We propose Multiverse, a language-conditioned multi-game level generator that enables cross-game level blending through textual specifications. The model learns a shared latent space aligning textual instructions and level structures, while a threshold-based multi-positive contrastive supervision links semantically related levels across games. This representation allows language to guide which structural characteristics should be preserved when combining content from different games, enabling controllable blending through latent interpolation and zero-shot generation from compositional textual prompts. Experiments show that the learned representation supports controllable cross-game level blending and significantly improves blending quality within the same game genre, while providing a unified representation for language-conditioned multi-game content generation.
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