[2605.05390] LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05390 (cs)
[Submitted on 6 May 2026]
Title:LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World
Authors:Nan Yang, Julian Straub, Fan Zhang, Richard Newcombe, Jakob Engel, Lingni Ma
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Abstract:Tracking 3D human motion from egocentric multi-camera headset is challenged by severe egomotion, partial visibility or occlusions and lack of training data. Existing methods designed for monocular video often require static or slowly-moving cameras and cannot efficiently leverage multi-view, calibrated and localized input. This makes them brittle and prone to fail on dynamic egocentric captures. We propose LAMP (Localization Aware Multi-camera People Tracking): a novel, simple framework to solve this via early disentanglement of observer and target motion. LAMP introduces a two-step process. First, we leverage the known device 6 DoF motion and calibration to convert detected 2D body keypoints from all cameras over a temporal window into a unified 3D world reference frame. Second, an end-to-end-trained spatio-temporal transformer fits 3D human motion directly to this 3D ray cloud. This "lift-then-fit" approach allows LAMP to learn and leverage a natural human motion prior in the world-space, as well as providing an elegant framework to flexibly incorporate information from multiple temporally asynchronous, partially observing and moving cameras. LAMP achieves state-of-the-art results on monocular benchmarks, while significantly outperforming baselines for our targeted egocentric setting.
Comments: CVPR 2026. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.05390 [cs.CV]
(or arXiv:2605.05390v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05390
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arXiv-issued DOI via DataCite (pending registration)
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From: Nan Yang [view email] [v1] Wed, 6 May 2026 19:23:22 UTC (21,037 KB)
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