With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual
(RGB) sensing has become available for widespread use. The complementary nature of the …

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 …

Estimating hand-object manipulations is essential for in-terpreting and imitating human
actions. Previous work has made significant progress towards reconstruction of hand poses …

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 present an approach that uses a multi-camera system to train fine-grained detectors for
keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure …

We present a unified deformation model for the markerless capture of multiple scales of
human movement, including facial expressions, body motion, and hand gestures. An initial …

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 …

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 …