EgoTraj: Real-World Egocentric Human Trajectory

Accurately forecasting human trajectories from an egocen-tric perspective plays a central role in applications such as humanoidrobotics, wearable sensing systems, and assistive navigation. However,progress in this direction remains limited due to the scarcity of egocen-tric trajectory datasets collected in real-world environments. Address-ing this need, we introduce EgoTraj, an egocentric multimodal opendataset recorded using Meta Quest Pro (MQPro). EgoTraj contains 75sequences of human navigation collected from multiple MQPro wearersin real-world urban environments. Each recording provides synchronizedRGB video along with ground-truth data, including 6-degree-of-freedomhead poses, per-frame 3D gaze vectors, and scene annotations. To thebest of our knowledge, EgoTraj differs from typical egocentric trajectorydatasets by capturing long-horizon, self-chosen pedestrian navigation onconsumer AR headset across urban routes with broad participant diver-sity. We benchmark several state-of-the-art trajectory prediction modelsand ablate the contributions of gaze, scene, and motion cues. The resultshighlight the utility of EgoTraj for AR-based perception, navigation, andassistive systems. The EgoTraj dataset, code, and EgoViz Dashboard arepublicly available at https://github.com/yehiahmad/EgoTraj.