Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection
This addresses the need for better hand segmentation and detection in applications like AR/VR, but it is incremental as it builds on existing data generation methods.
The authors tackled the problem of hand segmentation and detection in unconstrained RGB settings by introducing Ego2Hands, a large-scale dataset with semi-automatic annotation and a color-invariant data generation technique, resulting in models that generalize to unseen environments without domain adaptation.
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.