Abstract 3D hand pose estimation is still far from a well-solved problem mainly due to the
highly nonlinear dynamics of hand pose and the difficulties of modeling its inherent …

Robotic teleoperation, ie, manipulating remote robotic systems at a distance, has gained its
popularity in various industrial applications, including construction operations. The key to a …

Recent work achieved impressive progress towards joint reconstruction of hands and
manipulated objects from monocular color images. Existing methods focus on two …

Towards 3D object tracking in point clouds, a novel point-to-box network termed P2B is
proposed in an end-to-end learning manner. Our main idea is to first localize potential target …

We present a self-supervision method for 3D hand pose estimation from depth maps. We
begin with a neural network initialized with synthesized data and fine-tune it on real but …

Abstract 3D hand pose estimation has made significant progress recently, where
Convolutional Neural Networks (CNNs) play a critical role. However, most of the existing …

Hand pose estimation from monocular RGB inputs is a highly challenging task. Many
previous works for monocular settings only used RGB information for training despite the …

With increasing applications of 3D hand pose estimation in various human-computer
interaction applications, convolution neural networks (CNNs) based estimation models have …

In natural conversation and interaction, our hands often overlap or are in contact with each
other. Due to the homogeneous appearance of hands, this makes estimating the 3D pose of …

Depth images and point clouds are the two most commonly used data representations for
depth-based 3D hand pose estimation. Benefiting from the structuring of image data and the …