Estimating 3D hand meshes from single RGB images is challenging, due to intrinsic 2D-3D
mapping ambiguities and limited training data. We adopt a compact parametric 3D hand …

We present an approach for real-time, robust, and accurate hand pose estimation from
moving egocentric RGB-D cameras in cluttered real environments. Existing methods …

Abstract Convolutional Neural Network (CNN) has shown promising results for 3D hand
pose estimation in depth images. Different from existing CNN-based hand pose estimation …

DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations
of a hand given a depth map. Since its publication early 2015, it has been outperformed by …

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 …

Abstract Convolutional Neural Networks (CNNs)-based methods for 3D hand pose
estimation with depth cameras usually take 2D depth images as input and directly regress …

Hand pose estimation from single depth images is an essential topic in computer vision and
human computer interaction. Despite recent advancements in this area promoted by …

Deep learning solutions for hand pose estimation are now very reliant on comprehensive
datasets covering diverse camera perspectives, lighting conditions, shapes, and pose …

We propose a simple and efficient method for exploiting synthetic images when training a
Deep Network to predict a 3D pose from an image. The ability of using synthetic images for …

Despite recent successes in hand pose estimation, there yet remain challenges on RGB-
based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where …