{"ID":2850959,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20182","arxiv_id":"2510.20182","title":"Evaluating Video Models as Simulators of Multi-Person Pedestrian Trajectories","abstract":"Large-scale video generation models have demonstrated high visual realism in diverse contexts, spurring interest in their potential as general-purpose world simulators. Existing benchmarks focus on individual subjects rather than scenes with multiple interacting people. However, the plausibility of multi-agent dynamics in generated videos remains unverified. We propose a rigorous evaluation protocol to benchmark text-to-video (T2V) and image-to-video (I2V) models as implicit simulators of pedestrian dynamics. For I2V, we leverage start frames from established datasets to enable comparison with a ground truth video dataset. For T2V, we develop a prompt suite to explore diverse pedestrian densities and interactions. A key component is a method to reconstruct 2D bird's-eye view trajectories from pixel-space without known camera parameters. Our analysis reveals that leading models have learned surprisingly effective priors for plausible multi-agent behavior. However, failure modes like merging and disappearing people highlight areas for future improvement.","short_abstract":"Large-scale video generation models have demonstrated high visual realism in diverse contexts, spurring interest in their potential as general-purpose world simulators. Existing benchmarks focus on individual subjects rather than scenes with multiple interacting people. However, the plausibility of multi-agent dynamics...","url_abs":"https://arxiv.org/abs/2510.20182","url_pdf":"https://arxiv.org/pdf/2510.20182v1","authors":"[\"Aaron Appelle\",\"Jerome P. Lynch\"]","published":"2025-10-23T04:06:58Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}