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Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications

2022-08-07 21:43:40
Lingzhi Zhang, Shenghao Zhou, Simon Stent, Jianbo Shi
arXiv_CV
                 
Abstract

Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors. Hands and interacting objects are one crucial aspect of understanding a viewer's behaviors and intentions. We provide a labeled dataset consisting of 11,243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. Our dataset is the first to label detailed hand-object contact boundaries. We introduce a context-aware compositional data augmentation technique to adapt to out-of-distribution YouTube egocentric video. We show that our robust hand-object segmentation model and dataset can serve as a foundational tool to boost or enable several downstream vision applications, including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos. Dataset and code are available at: this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2208.03826

PDF

https://arxiv.org/pdf/2208.03826.pdf

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