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| license: cc-by-nd-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - Physical AI |
| - egocentric data |
| - robotics |
| - egocentric videos |
| - human motions |
| - action recognition |
| size_categories: |
| - 1K<n<10K |
| --- |
| # 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**. |
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| https://cdn-uploads.huggingface.co/production/uploads/66b9a1accb3c168701cb4c5a/1L4NqlWD0rBXLnrBg2BpC.mp4 |
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| 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](https://unidata.pro/datasets/egocentric-video/?utm_source=huggingface&utm_medium=referral&utm_campaign=egocentric-video)** |
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| 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. |
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| ## 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/egocentric-video/?utm_source=huggingface&utm_medium=referral&utm_campaign=egocentric-video) to discuss your requirements and pricing options. |
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| 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. |
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| Quaternion-based orientation from onboard sensor fusion supports 3D pose estimations and egocentric tracking across first-person perspectives. |
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| **Environments captured:** Kitchen, bathroom, living room, and other home environments. |
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| **Activities included:** Daily household actions, object transferring, hand-object interactions. |
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| **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. |
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| ## 🌐 [UniData](https://unidata.pro/datasets/egocentric-video/?utm_source=huggingface&utm_medium=referral&utm_campaign=egocentric-video) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects |
