AI 用「物理语言」想清楚再画世界
现在的视频生成模型是「直接画」:它预测下一帧像素，世界怎么变是藏在神经网络里的黑箱，你没法说「把球往左滚」它就真按物理来。这篇换了个思路：先让模型从海量视频里自学出一套「物理语言」——把世界状态的变化压缩成一个个离散符号，比如「物体A从位置1移到位置2」;然后它先像人一样用这套语言推演未来几秒会发生什么，推明白了，再把这套推演渲染成视频。结果是它生成的视频在物理连贯性上更稳，还能做精细的动作条件模拟和零样本动作迁移。它不是你明天就能用的工具，但这是视频生成从「画得像」走向「想得对」的一个方向。
📄 原文摘要(英文)
We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing physical world models typically predict future videos directly in pixel space, leaving the underlying world dynamics implicit within high-dimensional visual predictors. Motivated by humans' ability to abstract predictive structure from visual experience and organize it in natural language for explicit reasoning, we learn physical language from in-the-wild videos through self-supervision and use it to explicitly reason about how the physical world evolves. Accordingly, PhiZero adopts a reason-then-render paradigm: it first infers future world evolution as a physical-language sequence and then renders the inferred transitions into videos. Extensive experiments across generation and understanding benchmarks validate the ability of PhiZero to model physically coherent world evolution. We further show its potential for realistic and interactive world modeling, fine-grained action-conditioned simulation, and zero-shot motion transfer.