Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

The field of vision-based human hand three-dimensional (3D) shape and pose estimation
has attracted significant attention recently owing to its key role in various applications, such …

Abstract 3D hand-mesh reconstruction from RGB images facilitates many applications,
including augmented reality (AR). However, this requires not only real-time speed and …

Abstract 3D interacting hand pose estimation from a single RGB image is a challenging task,
due to serious self-occlusion and inter-occlusion towards hands, confusing similar …

Analyzing and understanding hand information from multimedia materials like images or
videos is important for many real world applications and remains to be very active in …

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

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 …

Abstract 3D hand pose estimation methods have made significant progress recently.
However, estimation accuracy is often far from sufficient for specific real-world applications …

Abstract 3D hand pose estimation from monocular videos is a long-standing and
challenging problem, which is now seeing a strong upturn. In this work, we address it for the …

We present SemiHand, a semi-supervised framework for 3D hand pose estimation from
monocular images. We pre-train the model on labelled synthetic data and fine-tune it on …