AlayaWorld: 长视界与可交互的视频世界生成

摘要

传统游戏世界通常通过劳动密集型生产流程构建，导致其开发成本高昂、难以实现个性化定制，且部署后修改成本极高。近期视频世界模型的突破性进展提供了一种截然不同的范式：这些模型无需逐一显式设计虚拟环境的每个组件，而是基于当前世界状态与用户交互，自回归地合成未来的观测结果，从而支持可交互世界的实时生成。通过同时利用游戏录屏与真实世界视频进行训练，这些模型能够捕捉多样的视觉表现与物理动态，为游戏之外的互动应用（包括具身智能）开辟了全新可能。本文提出 AlayaWorld——一个构建交互式生成世界的全栈开源框架。该框架支持开放式实时交互，允许用户自由探索并执行战斗、施法、召唤怪物等多样化动作。框架通过模块化可扩展架构，将从数据准备、模型设计、模型训练、推理加速到部署的完整开发流程统一整合。伴随框架发布，我们还提供可复现的流水线、参考实现、评估工具及详尽文档，为生成式世界模型的未来研究与实时应用奠定实践基础。

English

Game worlds have traditionally been built through labor-intensive production pipelines, making them costly to develop, difficult to customization, and expensive to modify after deployment. Recent advances in video world models offer a fundamentally different paradigm. Rather than explicitly authoring every component of a virtual environment, these models autoregressively synthesize future observations conditioned on the current world state and user interactions, enabling playable worlds to be generated online. Trained on both gameplay recordings and real-world videos, they can capture diverse visual appearances and physical dynamics, opening new opportunities for interactive applications beyond gaming, including embodied intelligence. In this paper, we present AlayaWorld, a full-stack open-source framework for building interactive generative worlds. AlayaWorld enables open-ended real-time interaction, allowing users to freely navigate and perform diverse actions such as combat, spell casting, and monster summoning. The framework unifies the complete development-from data preparation model architecture, model training, inference acceleration, and deployment-within a modular and extensible architecture. Alongside the framework, we release reproducible pipelines, reference implementations, evaluation tools, and comprehensive documentation, establishing a practical foundation for future research and real-time applications of generative world models.