CVPR 2026
Nan Yang · Julian Straub · Fan Zhang · Richard Newcombe · Jakob Engel · Lingni Ma
Meta Reality Labs Research
LAMP tracks 3D human motion from egocentric multi-camera headsets via early disentanglement of observer and target motion. Using known device 6-DoF motion and calibration, 2D body keypoints from all cameras over a temporal window are lifted into a unified 3D world reference frame, and an end-to-end trained spatio-temporal transformer fits 3D human motion directly to this 3D ray cloud. This "lift-then-fit" approach achieves state-of-the-art results on monocular benchmarks while significantly outperforming baselines on the targeted egocentric setting.
- Code release coming soon — please stay tuned!
@inproceedings{yang2026lamp,
title = {{LAMP}: Localization Aware Multi-camera People Tracking in Metric {3D} World},
author = {Yang, Nan and Straub, Julian and Zhang, Fan and Newcombe, Richard and Engel, Jakob and Ma, Lingni},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2026}
}LAMP is CC-BY-NC licensed, as found in the LICENSE file.