Paper presented at ECCV 2018



CAMERA was delighted to present HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision at ECCV 2018.

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.

Reference: Xiaokun Wu, Daniel Finnegan, Eamonn O’Neill, and Yong-Liang Yang. HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision. The European Conference on Computer Vision (ECCV), 2018, pp. 237-253.

You can read the full paper here http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper.pdf

 


Written by CAMERA Centre Coordinator