Datasets:
license: cc-by-4.0
task_categories:
- visual-question-answering
- summarization
- video-classification
- any-to-any
language:
- en
- de
pretty_name: IndEgo
tags:
- industrial
- egocentric
- procedural
- collaborative work
- mistake detection
- VQA
- video understanding
size_categories:
- 10K<n<100K
IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants
Project Page: https://vivek9chavan.github.io/IndEgo/
Abstract:
We introduce IndEgo, a multimodal egocentric and exocentric video dataset capturing common industrial tasks such as assembly/disassembly, logistics and organisation, inspection and repair, and woodworking.
The dataset includes 3,460 egocentric recordings (~197 hours) and 1,092 exocentric recordings (~97 hours).
A central focus of IndEgo is collaborative work, where two workers coordinate on cognitively and physically demanding tasks.
The egocentric recordings include rich multimodal data — eye gaze, narration, sound, motion, and semi-dense point clouds.
We provide:
- Detailed annotations: actions, summaries, mistake labels, and narrations
- Processed outputs: eye gaze, hand poses, SLAM-based semi-dense point clouds
- Benchmarks: procedural/non-procedural task understanding, Mistake Detection, and reasoning-based Video QA
Baseline evaluations show that IndEgo presents a challenge for state-of-the-art multimodal models.
Acknowledgements: Meta Reality Labs for their support and open-science initiative with Project Aria.
If you use IndEgo, please cite our NeurIPS 2025 paper:
@inproceedings{Chavan2025IndEgo,
author = {Vivek Chavan and Yasmina Imgrund and Tung Dao and Sanwantri Bai and Bosong Wang and Ze Lu and Oliver Heimann and J{\"o}rg Kr{\"u}ger},
title = {IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track},
year = {2025},
url = {https://neurips.cc/virtual/2025/poster/121501}
}