Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild

Lingni Ma, Yuting Ye, Fangzhou Hong, Vladimir Guzov, Yifeng Jiang +12 more
2/14/2026

No official code found. Best implementation: facebookresearch/nymeria_dataset (266 stars, last commit 2026-04-29).

Abstract

We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) full-body ground-truth motion; b) multiple multimodal egocentric data from Project Aria devices with videos, eye tracking, IMUs and etc; and c) a third-person perspective by an additional observer. All devices are precisely synchronized and localized in on metric 3D world. We derive hierarchical protocol to add in-context language descriptions of human motion, from fine-grain motion narration, to simplified atomic action and high-level activity summarization. To the best of our knowledge, Nymeria dataset is the world's largest collection of human motion in the wild; first of its kind to provide synchronized and localized multi-device multimodal egocentric data; and the world's largest motion-language dataset. It provides 300 hours of daily activities from 264 participants across 50 locations, total travelling distance over 399Km. The language descriptions contain 301.5K sentences in 8.64M words from a vocabulary size of 6545. To demonstrate the potential of the dataset, we evaluate several SOTA algorithms for egocentric body tracking, motion synthesis, and action recognition. Data and code are open-sourced for research (c.f. https://www.projectaria.com/datasets/nymeria).

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Nymeria: a massive collection of multimodal egocentric daily motion in the wild

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Cite this paper

@article{ma2026nymeria,
  title  = {Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild},
  author = {Lingni Ma and Yuting Ye and Fangzhou Hong and Vladimir Guzov and Yifeng Jiang and Rowan Postyeni and Luis Pesqueira and Alexander Gamino and Vijay Baiyya and Hyo Jin Kim and Kevin Bailey and David Soriano Fosas and C. K. Liu and Ziwei Liu and J. Engel and R. D. Nardi and Richard A. Newcombe},
  year   = {2026},
  eprint = {2406.09905},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2406.09905},
  journal = {ECCV 2024 2024}
}

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