---
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