{"ID":2921951,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T02:42:49.606572591Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00499","arxiv_id":"2606.00499","title":"OptiWorld: Optimal Control for Video World Generation under Physical Constraints","abstract":"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.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2606.00499","url_pdf":"https://arxiv.org/pdf/2606.00499v1","authors":"[\"Yu Yuan\",\"Jianhao Yuan\",\"Xijun Wang\",\"Daiqing Li\",\"Liu He\",\"Lu Ling\",\"Stanley H. Chan\"]","published":"2026-05-30T03:13:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}