EgoHOS (Fine-Grained Egocentric Hand-Object Segmentation Dataset)

EgoHOS is a labeled dataset consisting of 11243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. The data are collected form multiple sources: 7,458 frames from Ego4D, 2,212 frames from EPIC-KITCHEN, 806 frames from THU-READ, and 350 frames of our own collected egocentric videos with people playing Escape Room. This dataset is designed for tasks including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos.

Source: Fine-grained Egocentric Hand-object Segmentation: Dataset, Model, and Applications

Image Source: https://arxiv.org/pdf/2208.03826v1.pdf