HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision
Xiaokun Wu, Daniel Finnegan, Eamonn O'Neill, Yong-Liang Yang; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 237-253
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.
Related Material
[pdf]
[
bibtex]
@InProceedings{Wu_2018_ECCV,
author = {Wu, Xiaokun and Finnegan, Daniel and O'Neill, Eamonn and Yang, Yong-Liang},
title = {HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}