  In this paper, a feature boosting network is proposed for estimating 3D hand
pose and 3D body pose from a single RGB image. In this method, the features
learned by the convolutional layers are boosted with a new long short-term
dependence-aware (LSTD) module, which enables the intermediate convolutional
feature maps to perceive the graphical long short-term dependency among
different hand (or body) parts using the designed Graphical ConvLSTM. Learning
a set of features that are reliable and discriminatively representative of the
pose of a hand (or body) part is difficult due to the ambiguities, texture and
illumination variation, and self-occlusion in the real application of 3D pose
estimation. To improve the reliability of the features for representing each
body part and enhance the LSTD module, we further introduce a context
consistency gate (CCG) in this paper, with which the convolutional feature maps
are modulated according to their consistency with the context representations.
We evaluate the proposed method on challenging benchmark datasets for 3D hand
pose estimation and 3D full body pose estimation. Experimental results show the
effectiveness of our method that achieves state-of-the-art performance on both
of the tasks.
