# Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection

arXiv: https://arxiv.org/abs/2011.07252 · HTML: https://mlsanity.com/paper/2011.07252/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications](https://mlsanity.com/paper/2208.03826/) (2022)
- [EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views](https://mlsanity.com/paper/2405.13659/) (2024)
- [Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentation](https://mlsanity.com/paper/2410.01341/) (2024)
- [Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation](https://mlsanity.com/paper/2107.02718/) (2021)
- [Ego2HandsPose: A Dataset for Egocentric Two-hand 3D Global Pose Estimation](https://mlsanity.com/paper/2206.04927/) (2022)
- [FEEL (Force-Enhanced Egocentric Learning): A Dataset for Physical Action Understanding](https://mlsanity.com/paper/2603.15847/) (2026)
- [Gaze Beyond the Frame: Forecasting Egocentric 3D Visual Span](https://mlsanity.com/paper/2511.18470/) (2025)
- [Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks](https://mlsanity.com/paper/2102.01460/) (2021)
- [Testing Human-Hand Segmentation on In-Distribution and Out-of-Distribution Data in Human-Robot Interactions Using a Deep Ensemble Model](https://mlsanity.com/paper/2501.07713/) (2025)

## Abstract (arXiv)

Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications. However, existing datasets are far from sufficient in terms of size and variety due to the infeasibility of manual annotation of large amounts of segmentation and detection data. As a result, current methods are limited by many underlying assumptions such as constrained environment, consistent skin color and lighting. In this work, we present Ego2Hands, a large-scale RGB-based egocentric hand segmentation/detection dataset that is semi-automatically annotated and a color-invariant compositing-based data generation technique capable of creating training data with large quantity and variety. For quantitative analysis, we manually annotated an evaluation set that significantly exceeds existing benchmarks in quantity, diversity and annotation accuracy. We provide cross-dataset evaluation as well as thorough analysis on the performance of state-of-the-art models on Ego2Hands to show that our dataset and data generation technique can produce models that generalize to unseen environments without domain adaptation.