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UnidataRobotics/egocentric-video

Egocentric Dataset for Physical AI and Robotics The dataset contains 4,050 hours of first-person videos for egocentric vision and egocentric tracking. Featuring multimodal data from egocentric views, it includes data annotations and motion capture for extracting 3d poses. It provides detailed 3d objects and 3d scenes using visual data from VR headsets to analyze hands motions and pose estimations. .- Get the data Dataset characteristics: Characteristic Data… See the full description on the dataset page: https://huggingface.co/datasets/UnidataRobotics/egocentric-video.

sourceHugging Facecc-by-nc-nd-4.0updated 4mo agoView on Hugging Face
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Egocentric Dataset for Physical AI and Robotics

The dataset contains 4,050 hours of first-person videos for egocentric vision and egocentric tracking. Featuring multimodal data from egocentric views, it includes data annotations and motion capture for extracting 3d poses. It provides detailed 3d objects and 3d scenes using visual data from VR headsets to analyze hands motions and pose estimations. .- [Get the data](https://unidata.pro/datasets/egocentric-video/?utm_source=hf-robotics&utm_medium=referral&utm_campaign=egocentric-video)

Dataset characteristics:

CharacteristicData
DescriptionEgocentric video recordings of daily activities in home environments
Data typesVideo
TasksHand Activity Recognition Egocentric Action Recognition Hand-Object Interaction
Hours of recordings4,050
Hardware setups2
Setup 1 (Pico + Motion Trackers)2,321 hours (57.3%) — natural speed, slow-motion, and real-speed object transferring
Setup 2 (Zed + Pico + Motion Trackers)1,729 hours (42.7%) — scripted object transfer tasks with spatial depth + egocentric view
Scenarios13 (sorting unsorted items, arranging products by category, collecting items into a container, transferring from drawer to table, wardrobe & table & bag, transport box & display table, folding fabric items, lids & cookware & drawers, transferring with a spoon, transferring with tongs, packing into containers, two-handed sorting, assembly & disassembly)
EnvironmentsKitchen, bathroom, living room, and other home settings
ActivitiesSorting, transferring, folding, assembly/disassembly, tool use, two-handed manipulation

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Dataset structure

  • —Setup 1 (Pico + Motion Trackers): 2,321 hours (57.3%) — natural speed, slow-motion, and real-speed object transferring, with hands appearing as needed or always in frame for detailed kinematics.
  • —Setup 2 (Zed + Pico + Motion Trackers): 1,729 hours (42.7%) — scripted object transfer tasks combining spatial depth from stereo Zed cameras with egocentric view from Pico headset.

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Similar Datasets:

  1. 1.Robotic Household Activities Dataset
  2. 2.Lerobot SO-101 Manipulations Dataset
  3. 3.Scene Scanning Video Dataset

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