4 accepted papers
While text-to-video (T2V) generation has achieved remarkable progress in photorealism, generating intent-aligned videos that faithfully obey physics principles remains a core challenge. In this work, we systematically study Newtonian motion-controlled text-to-video generation and evaluation, emphasi
We present UniTrack, a plug-and-play graph-theoretic loss function designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified differentiable learning. Unlike prior graph-based MOT methods that redesign tracking archit…
Diffusion models have proven effective at generating high-quality images from learned distributions, but their application to the temporal domain, especially for driving scenarios, remains underexplored. Our work addresses key challenges in existing simulations, such as limited data quality, diversi
Evaluating autonomous ground vehicles requires evaluating their mobility performance. Since autonomous vehicles are envisioned to make decisions in a variety of situations and environments too diverse to practically assess with only physical testing, their development, and evaluation will necessaril…