In this paper we introduce a large-scale hand pose dataset, collected using a novel capture
method. Existing datasets are either generated synthetically or captured using depth …

In the recent years, a steady growth of Hand Gesture Recognition (HGR) based applications
has been observed. Thus, significant progress has been made in the field of hand detection …

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D
model of human body pose, hand pose, and facial expression from a single monocular …

This work addresses a novel and challenging problem of estimating the full 3D hand shape
and pose from a single RGB image. Most current methods in 3D hand analysis from …

We present a learning framework for recovering the 3D shape, camera, and texture of an
object from a single image. The shape is represented as a deformable 3D mesh model of an …

To understand how people look, interact, or perform tasks, we need to quickly and
accurately capture their 3D body, face, and hands together from an RGB image. Most …

We present in this work the first end-to-end deep learning based method that predicts both
3D hand shape and pose from RGB images in the wild. Our network consists of the …

There has been significant work on learning realistic, articulated, 3D models of the human
body. In contrast, there are few such models of animals, despite many applications. The …

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 …

Humans move their hands and bodies together to communicate and solve tasks. Capturing
and replicating such coordinated activity is critical for virtual characters that behave …