Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation

Abstract

Abstract. Discriminative methods often generate hand poses kinematically implausible, then generative methods are used to correct (or verify) these results in a hybrid method. Estimating 3D hand pose in a hierarchy, where the high-dimensional output space is decomposed into smaller ones, has been shown effective. Existing hierarchical methods mainly focus on the decomposition of the output space while the input space remains almost the same along the hierarchy. In this paper, a hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO). The spatial attention mechanism integrates cascaded and hierarchical regression into a CNN framework by transforming both the input(and feature space) and the output space, which greatly reduces the viewpoint and articulation variations. Between the levels in the hierarchy, the hierarchical PSO forces the kinematic constraints to the results of the CNNs. The experimental results show that our method significantly outperforms four state-of-the-art methods and three baselines on three public benchmarks.

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“…hand gesture as a primary interface for AR/VR. The problem is challenging due to high dimensionality of hand space, pose and shape variations, self-occlusions, etc [52,43,58,65,10,61,40,5,12,63,47,59,37,67,54,53,9,44,31,49,32,20,27,33,7,64]. Most existing methods have focused on recovering sparse hand poses i.e.…”

Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation

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Point-To-Pose Voting Based Hand Pose Estimation Using Residual Permutation Equivariant Layer

“…Generative methods rely on a hand model and an optimization method to fit the hand model to the observations [ [28]. Hybrid methods use a combination of the generative and discriminative methods [18] [27] [34]. Our method is a learning based method thus falls into the second category.…”