让视频生成具备物理合理性，自动规划安全高效的运动轨迹。
OptiWorld: Optimal Control for Video World Generation under Physical Constraints
- 引入最优控制理论，在生成时动态规划受物理约束的轨迹。
- 在目标引导生成等任务中，视频运动更平滑、安全且符合物理规律。
- 适合需要真实动力学模拟的场景，如机器人路径规划、影视特效编辑。
视频生成模型正成为可扩展的世界模型形式，但目前主要生成看似合理的运动，而非主动控制或优化底层动力学。因此，生成视频中的物体可能遵循不安全、不平滑、低效或物理上不一致的轨迹。本文提出 extbf{OptiWorld}，一个在推理阶段引入经典最优控制的视频世界生成框架。OptiWorld 首先提取紧凑的任务相关世界状态，然后在物理约束下规划最优轨迹，并最终根据该轨迹渲染视频。我们将规划建模为连续流形上的几何问题，将三维几何与任务相关的物理约束统一为规划几何。通过增加这一最优控制层，OptiWorld 生成的视频展现出更优的动力学特性，在目标条件图像到视频生成、视频动力学编辑和反事实生成等多个任务中均表现出色。
原文摘要 · Abstract (English)
Video generation models are becoming a scalable form of world models, but they mainly generate plausible motion rather than proactively control or optimize the underlying dynamics. As a result, an object in the generated video may follow trajectories that are unsafe, not smooth, inefficient, or physically inconsistent. In this work, we propose \textbf{OptiWorld}, a framework that brings classical optimal control into video generation at inference time. OptiWorld first extracts a compact, task-relevant world state, then plans an optimal trajectory under physical constraints, and finally renders the video conditioned on this trajectory. We formulate planning as a geometric problem on a continuous manifold, which converts 3D geometry and task-dependent physical constraints into a unified planning geometry. By adding this optimal-control layer, OptiWorld generates videos with preferable dynamics, demonstrating strong potential in multiple tasks including goal-conditioned image-to-video generation, video dynamics editing, and counterfactual generation.
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