一张显卡跑出无限游戏世界
你不需要买几万块的服务器，一张RTX 5090显卡就能实时生成一个无限大的、可交互的3D世界。研究者训练了一个AI世界模型，它学会了你按键盘的动作和画面变化之间的关系——你按W它就向前走，按鼠标它就转头，画面实时生成，延迟只有1.2秒。它从AAA游戏、模拟引擎和网络视频里学来了各种场景和物理规律，所以生成的画面不是贴图，而是AI实时算出来的。你可以在里面自由探索，角色外观也能保持一致。这不是明天就能玩的游戏，但它告诉你：未来的游戏可能不再需要下载几十GB的安装包，而是由AI在你电脑上实时生成。
📄 原文摘要(英文)
We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA games, simulation engines, and internet videos to learn controllable world dynamics. WorldExplorer performs agent-driven collection guided by training feedback, while a unified pipeline applies 14 deterministic quality checks, VLM-based assessment, and synchronized action and text annotation. We progressively distill a bidirectional action-conditioned teacher into a causal student through teacher forcing and ODE distillation, and introduce LongForcing to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift. Raw keyboard actions provide a unified control interface for scene roaming and third-person character interaction, while reference-character memory provides persistent appearance cues for identity consistency during third-person rollouts. For deployment, we co-design a streaming inference stack with a lightweight VAE decoder, efficient attention, memory-aware scheduling, and low-bit DiT inference. Across optimized low-bit configurations, ABot-World-0 streams 720P video at up to 16 FPS on a single NVIDIA RTX 5090 desktop GPU, with 1.2s action-to-first-frame latency and approximately 19GiB peak VRAM. Experiments on WorldRoamBench and extended interactive rollouts demonstrate competitive controllability and coherent long-horizon world evolution.