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

Due to the constantly increasing demand for the automatic localization of landmarks in hand
gesture recognition, there is a need for a more sustainable, intelligent, and reliable system …

This paper deals with a text-to-video sign language synthesis. Instead of direct video
production, we focused on skeletal models production. Our main goal in this paper was to …

Estimating 3D hand poses from a single RGB image is challenging because depth
ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh …

Estimating3D hand poses from RGB images is essentialto a wide range of potential
applications, but is challengingowing to substantial ambiguity in the inference of depth in …

Estimating 3D hand pose directly from RGB images is challenging but has gained steady
progress recently by training deep models with annotated 3D poses. However annotating …

Although 3D hand pose estimation has made significant progress in recent years with the
development of the deep neural network, most learning-based methods require a large …

Estimating the 3D hand pose from a monocular RGB image is important but challenging. A
solution is training on large-scale RGB hand images with accurate 3D hand keypoint …

Estimating the 3D hand pose from a monocular RGB image is important but challenging. A
solution is training on large-scale RGB hand images with accurate 3D hand keypoint …

Recently, graph convolution networks have become the mainstream methods in 3D hand
pose and mesh estimation, but there are still some issues hindering its further development …