Initial public release

Co-authored-by: meta-bot <meta-bot@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-nc-4.0
+datasets:
+- facebook/LAMP
+---
+
+<div align="center">
+
+💡LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World
+
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://facebookresearch.github.io/LAMP)
+[![arXiv](https://img.shields.io/badge/arXiv-2605.05390-b31b1b)](https://arxiv.org/abs/2605.05390)
+[![Video](https://img.shields.io/badge/Video-YouTube-red)](https://youtu.be/pJv1xJ-ssUQ)
+
+**CVPR 2026**
+
+[Nan Yang](https://nan-yang.me/) · [Julian Straub](https://jstraub.github.io/) · [Fan Zhang]() · [Richard Newcombe](https://rapiderobot.bitbucket.io/) · [Jakob Engel](https://jakobengel.github.io/) · [Lingni Ma](https://scholar.google.com/citations?user=eUAgpwkAAAAJ&hl=en)
+
+*Meta Reality Labs Research*
+
+</div>
+
+![LAMP teaser](https://github.com/facebookresearch/LAMP/raw/main/resources/imgs/lamp_teaser.png)
+
+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.
+
+
+## Citation
+
+```bibtex
+@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_smpl_aria_gen2.pt

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