Video Number int64 | Room string | Scenario string | Main Actions string | Lighting string |
|---|---|---|---|---|
1 | Kitchen | Washing dishes | Approach the sink; wash the dishes; wipe/dry the dishes; put the dishes away into cabinets/drawers | Natural |
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.
By utilizing this dataset, researchers and developers can advance egocentric vision systems, train robotic manipulation policies, and benchmark object detection algorithms on real-world first-person footage with precise 3D pose annotations. - Get the data
Captured via VR headsets and 4 Zed cameras, the dataset integrates multimodal data including IMU signals and quaternion-based orientation from onboard sensors to support pose estimations and 3D reconstructions.
💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.
The videos were captured via two setups across multiple scenarios:
- 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.
Quaternion-based orientation from onboard sensor fusion supports 3D pose estimations and egocentric tracking across first-person perspectives.
Environments captured: Kitchen, bathroom, living room, and other home environments.
Activities included: Daily household actions, object transferring, hand-object interactions.
Scenarios (13 total): 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.
🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects
- Downloads last month
- 56