HandMap: Robust Hand Pose Estimation via
Intermediate Dense Guidance Map Supervision
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 stateof-the-art in both 2D and 3D on the recent benchmark datasets.