3 papers
cs.CV2026
Modeling 3D Pedestrian-Vehicle Interactions for Vehicle-Conditioned Pose Forecasting
Guangxun Zhu, Xuan Liu, Nicolas Pugeault +2
Accurately predicting pedestrian motion is crucial for safe and reliable autonomous driving in complex urban environments. In this work, we present a 3D vehicle-conditioned pedestr…
cs.CV2025
Waymo-3DSkelMo: A Multi-Agent 3D Skeletal Motion Dataset for Pedestrian Interaction Modeling in Autonomous Driving
Guangxun Zhu, Shiyu Fan, Hang Dai +1
Large-scale high-quality 3D motion datasets with multi-person interactions are crucial for data-driven models in autonomous driving to achieve fine-grained pedestrian interaction u…
cs.GR2025
Multi-Person Interaction Generation from Two-Person Motion Priors
Wenning Xu, Shiyu Fan, Paul Henderson +1
Generating realistic human motion with high-level controls is a crucial task for social understanding, robotics, and animation. With high-quality MOCAP data becoming more available…