HandMap: robust hand pose estimation via intermediate dense guidance map supervision

Wu, Xiaokun, Finnegan, Daniel ORCID: https://orcid.org/0000-0003-1169-2842, O'Neill, Eamonn and Yang, Yong-Liang 2018. HandMap: robust hand pose estimation via intermediate dense guidance map supervision. Presented at: ECCV 2018: 15th European Conference on Computer Vision, Munich, Germany, 8-14 September 2018. Published in: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian and Weiss, Yair eds. Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XVI. Lecture Notes in Computer Science. Lecture Notes in Computer Science Cham, Switzerland: Springer Verlag, pp. 246-262. 10.1007/978-3-030-01270-0_15

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Abstract

This work presents a novel hand pose estimation framework via intermediate dense guidance map supervision. By leveraging the advantage of predicting heat maps of hand joints in detection-based methods, we propose to use dense feature maps through intermediate supervision in a regression-based framework that is not limited to the resolution of the heat map. Our dense feature maps are delicately designed to encode the hand geometry and the spatial relation between local joint and global hand. The proposed framework significantly improves the state-of-the-art in both 2D and 3D on the recent benchmark datasets.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer Verlag
ISBN:	9783030012694
ISSN:	0302-9743
Funders:	EPSRC
Date of First Compliant Deposit:	15 July 2019
Date of Acceptance:	8 September 2018
Last Modified:	26 Oct 2022 07:11
URI:	https://orca.cardiff.ac.uk/id/eprint/124221

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