Successful efforts in hand gesture recognition research within the last two decades paved
the path for natural human–computer interaction systems. Unresolved challenges such as …

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 We present Deep Global Registration, a differentiable framework for pairwise
registration of real-world 3D scans. Deep global registration is based on three modules: a 6 …

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 …

Despite great progress in 3D pose estimation from single-view images or videos, it remains
a challenging task due to the substantial depth ambiguity and severe self-occlusions …

We propose a method for annotating images of a hand manipulating an object with the 3D
poses of both the hand and the object, together with a dataset created using this method …

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 …

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 …

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 …

In this work we study the use of 3D hand poses to recognize first-person dynamic hand
actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences …