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.gitattributes

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CameraRecord_20260401_102207.mp4

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Egocentric.csv

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+"
+Video Number",Room,Scenario,Main Actions,Lighting
+1,Kitchen,Washing dishes,Approach the sink; wash the dishes; wipe/dry the dishes; put the dishes away into cabinets/drawers,Natural

README.md

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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**. 
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/66b9a1accb3c168701cb4c5a/uB_Nu-5e3iA8MUqtTNZNs.png)
+
+https://cdn-uploads.huggingface.co/production/uploads/66b9a1accb3c168701cb4c5a/1L4NqlWD0rBXLnrBg2BpC.mp4
+
+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)**
+
+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](https://unidata.pro/datasets/egocentric-video/?utm_source=huggingface&utm_medium=referral&utm_campaign=egocentric-video) 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.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/66b9a1accb3c168701cb4c5a/w_xxAjEZNAJ-j6ptUcBig.png)
+
+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](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

trackingData_20260401_102207.txt

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