Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild
<|reference_start|>Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild: 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).<|reference_end|>
@article{ma2024nymeria:,
    title={Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in
  the Wild},
    author={Lingni Ma, Yuting Ye, Fangzhou Hong, Vladimir Guzov, Yifeng Jiang,
  Rowan Postyeni, Luis Pesqueira, Alexander Gamino, Vijay Baiyya, Hyo Jin Kim,
  Kevin Bailey, David Soriano Fosas, C. Karen Liu, Ziwei Liu, Jakob Engel,
  Renzo De Nardi, and Richard Newcombe},
    journal={arXiv preprint arXiv:2406.09905},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09905},
    primaryClass={cs.CV cs.GR}
}