Eidon built a wearable rig for capturing human manipulation: a head-mounted camera plus a seven-point IMU harness. We ran it across ordinary household work like laundry, cleaning, dishes and cooking.
The company has wound down. Everything we collected is published here under CC-BY-4.0, so it outlives us.
The data sits in three places. They are parts of one corpus rather than versions of it, and most people will want the first two together.
| What's inside | Size | Load with | |
|---|---|---|---|
| tracker-pov | The videos. 13,451 egocentric MP4s and one metadata row each | 9.05 TB | load_dataset |
| tracker-pov-imu | The sensors. 779M rows of 24 Hz orientation and motion for the same recordings | 9.5 GB | load_dataset |
| egocentric-pov | Extra egocentric video with no sensor data | 1.55 TB | hf buckets sync |
The third one is a Storage Bucket rather than a dataset, so it will not show up in the Datasets
list above and load_dataset cannot read it. Reach it with hf buckets sync or the
S3-compatible API.
tracker-pov and tracker-pov-imu cover the same 13,451 recordings, joined on recording_id.
Take the video, the sensors, or both.
from datasets import load_dataset
video = load_dataset("eidon-ai/tracker-pov", split="train") # 9.05 TB
imu = load_dataset("eidon-ai/tracker-pov-imu", split="train", streaming=True) # 9.5 GB
Filter the metadata first. It is small, and it carries the QC scores, task labels and sensor coverage flags you will want to select on:
import pandas as pd
meta = pd.read_parquet("hf://datasets/eidon-ai/tracker-pov/recordings/metadata.parquet")
good = meta[(meta.qc_status == "valid") & (meta.has_chest)]
1,274 hours of paired egocentric video and seven-point arm tracking, from 27 contributors in their own homes. Every recording passed an automated QC pass for hand presence, lighting, blur and camera stability, and ships with its scores.
The bucket holds another 306 hours of egocentric video across 1,370 recordings, from 37
contributors, 14 of whom also appear in the tracker set and keep the same contributor_id. Same
rig and same QC pipeline, without the IMU harness. Being a bucket, it is downloaded rather than
loaded: hf buckets sync hf://buckets/eidon-ai/egocentric-pov ./egocentric-pov.
Read the limitations section
before training on it. The short version: the task distribution leans heavily toward folding
laundry, contributors are few and unevenly weighted, and you should split by contributor_id
rather than randomly.
CC-BY-4.0. Commercial use allowed, attribution required. Published by Solidic Labs Inc (Eidon AI).
For removal requests or questions: padilla.samuelk@gmail.com