2026-05-20原文

EgoTraj: Real-World Egocentric Human Trajectory Dataset for Multimodal Prediction

Researchers introduce EgoTraj, an egocentric multimodal dataset recorded with Meta Quest Pro, featuring 75 navigation sequences in real-world urban environments with synchronized RGB video, head poses, eye gaze, and scene annotations. The dataset aims to advance trajectory prediction for humanoid robotics, wearable sensing, and assistive navigation, and benchmarks multiple state-of-the-art methods.

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Key points

EgoTraj is the first egocentric multimodal human trajectory dataset captured in real-world urban settings using Meta Quest Pro.
It contains 75 sequences with synchronized RGB video, 6-DoF head poses, 3D eye gaze vectors, and scene annotations.
The dataset captures long-horizon, self-directed navigation with diverse participants, distinguishing it from existing datasets.
Benchmarking results demonstrate EgoTraj's utility for AR-based perception, navigation, and assistive systems.

Why it matters

This matters because egoTraj is the first egocentric multimodal human trajectory dataset captured in real-world urban settings using Meta Quest Pro.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2605.19004] EgoTraj: Real-World Egocentric Human Trajectory Dataset for Multimodal Prediction

[Submitted on 18 May 2026]

Title:EgoTraj: Real-World Egocentric Human Trajectory Dataset for Multimodal Prediction

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Abstract:Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensing systems, and assistive navigation. However, progress in this direction remains limited due to the scarcity of egocentric trajectory datasets collected in real-world environments. Addressing this need, we introduce EgoTraj, an egocentric multimodal open dataset recorded using Meta Quest Pro (MQPro). EgoTraj contains 75 sequences of human navigation collected from multiple MQPro wearers in real-world urban environments. Each recording provides synchronized RGB video along with ground-truth data, including continuous time-synchronized 6-degree-of-freedom head poses, per-frame 3D eye gaze vectors, scene annotations. To the best of our knowledge, EgoTraj differs from typical egocentric trajectory datasets by capturing long-horizon, self-directed navigation across diverse urban routes with broad participant diversity. To demonstrate the potential of the dataset, we benchmark several state-of-the-art methods for egocentric trajectory prediction and conduct ablation studies to analyze the contributions of gaze, scene, and motion cues. The results highlight the utility of EgoTraj for AR-based perception, navigation, and assistive systems. The EgoTraj dataset, code, and EgoViz Dashboard are publicly available at this https URL.

Comments: 21 pages, 14 figures. Project page: this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)

ACM classes: I.2.10; I.4.8; I.5.4

Cite as: arXiv:2605.19004 [cs.CV]

(or arXiv:2605.19004v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.19004

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ahmad Yehia [view email] [v1] Mon, 18 May 2026 18:26:51 UTC (44,620 KB)

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