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 …

Abstract Analysis of hand-hand interactions is a crucial step towards better understanding
human behavior. However, most researches in 3D hand pose estimation have focused on …

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 …

We address the highly challenging problem of real-time 3D hand tracking based on a
monocular RGB-only sequence. Our tracking method combines a convolutional neural …

Most of the existing deep learning-based methods for 3D hand and human pose estimation
from a single depth map are based on a common framework that takes a 2D depth map and …

Estimating the 3D pose of a hand is an essential part of human-computer interaction.
Estimating 3D pose using depth or multi-view sensors has become easier with recent …

Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D
hand pose from monocular RGB images, due to substantial depth ambiguity and the …

The human hand moves in complex and high-dimensional ways, making estimation of 3D
hand pose configurations from images alone a challenging task. In this work we propose a …

Estimating 3D hand meshes from single RGB images is challenging, due to intrinsic 2D-3D
map** 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 …