The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Egocentric 3D human pose estimation with a single head-mounted fisheye camera has
recently attracted attention due to its numerous applications in virtual and augmented reality …

This article is concerned with the distributed set-membership fusion estimation problem for a
class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism …

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is
challenging especially under scenarios with varied illuminations and fast motions. Event …

Despite the impressive performance obtained by recent single-image hand modeling
techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This …

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 …

Previous 3D hand pose estimation methods primarily rely on a single modality, either RGB
or depth, and the comprehensive utilization of the dual modalities has not been extensively …

Hand pose estimation has recently emerged as a compelling topic in the robotic research
community, because of its usefulness in learning from human demonstration or safe human …

Realistic reconstruction of two hands interacting with objects is a new and challenging
problem that is essential for building personalized Virtual and Augmented Reality …

The extraction of keypoint positions from input hand frames, known as 3D hand pose
estimation, is crucial for various human-computer interaction applications. However, current …