Generating Parallel Worlds of a Flight

We study parallel worlds of a flight: visually distinct videos that faithfully reproduce a prescribed trajectory's shape, orientation, and temporal progression. Pre-trained video models provide rich visual priors for these worlds, but can produce convincing camera motion that drifts from the prescribed flight. We present WorldSlider, a generative pipeline that addresses this control challenge. Existing camera controls require models to translate geometric instructions into visual motion, while source-video references also carry the original world's appearance and geometry. WorldSlider's FlightGrid representation bridges this gap by automatically rendering calibrated camera poses as an appearance-free control video of the prescribed trajectory and its surrounding flight corridor. We distill the generator to four inference steps and refine flight alignment through reinforcement learning with feedback from a camera-motion verifier. WorldSlider improves trajectory fidelity over existing methods on standard trajectories and diverse flights, with qualitative demonstrations of transfer between simulated and real inputs. Its task-consistent visual diversification provides training data for visuomotor policies, substantially improving task success in simulation. Code and synchronized supplementary videos are available on https://github.com/parallelworldsanon/WorldSlider.