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 …

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 …

Estimating 3D hand and object pose from a single image is an extremely challenging
problem: hands and objects are often self-occluded during interactions, and the 3D …

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 …

We present a unified framework for understanding 3D hand and object interactions in raw
image sequences from egocentric RGB cameras. Given a single RGB image, our model …

Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a hand and of a
held object. In this paper, we propose a lightweight model called HOPE-Net which jointly …

Estimating 3D hand meshes from single RGB images is challenging, due to intrinsic 2D-3D
mapping ambiguities and limited training data. We adopt a compact parametric 3D hand …

The rise of deep learning has brought remarkable progress in estimating hand geometry
from images where the hands are part of the scene. This paper focuses on a new problem …

In this paper, we strive to answer two questions: What is the current state of 3D hand pose
estimation from depth images? And, what are the next challenges that need to be tackled …