Since the emergence of large annotated datasets, state-of-the-art hand pose estimation methods have been mostly based on discriminative learning. Recently, a hybrid approach has embedded a kinematic layer into the deep learning structure in such a way that the pose estimates obey the physical constraints of human hand kinematics. However, the existing approach relies on a single person's hand shape parameters, which are fixed constants. Therefore, the existing hybrid method has problems to generalize to new, unseen hands. In this work, we extend the kinematic layer to make the hand shape parameters learnable. In this way, the learnt network can generalize towards arbitrary hand shapes. Furthermore, inspired by the idea of Spatial Transformer Networks, we apply a cascade of appearance normalization networks to decrease the variance in the input data. The input images are shifted, rotated, and globally scaled to a similar appearance. The effectiveness and limitations of our proposed approach are extensively evaluated on the Hands 2017 challenge dataset and the NYU dataset.
2) Another strategy is to enforce the hand geometry by appropriate post-processing of estimates @cite_19 @cite_30 . In @cite_19 , the output joint location heat maps are refined to 3D joint locations by optimizing a hand model using Particle Swarm Optimization (PSO). However, such post-processing that is separated from the training procedure is sub-optimal. @cite_30 minimize an energy function using PSO for enforcing kinematic constraints between the kinematic hierarchy levels of their hierarchical hand pose estimation approach.
@cite_30 @cite_19
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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. We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.