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

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Computer Vision – ECCV 2022 (ECCV 2022)

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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: https://github.com/owenzlz/EgoHOS.

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Notes

  1. 1.

    MMSegmentation github: https://github.com/open-mmlab/mmsegmentation.

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This research is based on work supported by Toyota Research Institute and Adobe Gift Fund. The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsors.

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Zhang, L., Zhou, S., Stent, S., Shi, J. (2022). Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13689. Springer, Cham. https://doi.org/10.1007/978-3-031-19818-2_8

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