EnactPhys: Executing Evolving Physical Processes in Video Diffusion

Physical control in video generation requires translating user-specified parameters into corresponding motion responses. These responses arise through an evolving physical process in which parameter effects depend on the current state. Existing parameter-conditioning approaches leave this intermediate process largely implicit. Motivated by this task structure, we introduce **EnactPhys**, a video diffusion architecture for **state-mediated physical-process execution**. In our rigid-body instantiation, object states evolve through parameter-conditioned updates and inter-object interactions. We also introduce **PhysDelta**, a benchmark that jointly measures the **accuracy** and **selectivity** of physical control in simulation and matched real-world experiments. EnactPhys leads all four PhysDelta control metrics, improving over the strongest baseline for each metric by **15.0%–37.7%**, while maintaining competitive video quality and physical plausibility. It achieves the highest Verified Score on Physics-IQ (Solid Mechanics) among the evaluated methods, and ranks first in Invariance Score and second in Dynamical Score on MORPHEUS. State intervention experiments further show that learned state transformations transfer to held-out inputs and steer generated motion toward that induced by the target parameters, while keeping the original physical parameters fixed. Project page: https://enactphys.github.io.