HBE: Hand Branch Ensemble Network for Real-time 3D Hand Pose Estimation

Yidan Zhou, Jian Lu, Kuo Du, Xiangbo Lin, Yi Sun, Xiaohong Ma; The European Conference on Computer Vision (ECCV), 2018, pp. 501-516


The goal of this paper is to estimate the 3D coordinates of the hand joints from a single depth image. To give consideration to both the accuracy and the real time performance, we design a novel three-branch Convolutional Neural Networks named Hand Branch Ensemble network (HBE), where the three branches correspond to the three parts of a hand: the thumb, the index finger and the other fingers. The structural design inspiration of the HBE network comes from the understanding of the differences in the functional importance of different fingers. In addition, a feature ensemble layer along with a low-dimensional embedding layer ensures the overall hand shape constraints. The experimental results on three public datasets demonstrate that our approach achieves comparable or better performance to state-of-the-art methods with less training data, shorter training time and faster frame rate.

Related Material

author = {Zhou, Yidan and Lu, Jian and Du, Kuo and Lin, Xiangbo and Sun, Yi and Ma, Xiaohong},
title = {HBE: Hand Branch Ensemble Network for Real-time 3D Hand Pose Estimation},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}