  We focus on the task of everyday hand pose estimation from egocentric
viewpoints. For this task, we show that depth sensors are particularly
informative for extracting near-field interactions of the camera wearer with
his/her environment. Despite the recent advances in full-body pose estimation
using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D
images is still an unsolved problem. The problem is considerably exacerbated
when analyzing hands performing daily activities from a first-person viewpoint,
due to severe occlusions arising from object manipulations and a limited
field-of-view. Our system addresses these difficulties by exploiting strong
priors over viewpoint and pose in a discriminative tracking-by-detection
framework. Our priors are operationalized through a photorealistic synthetic
model of egocentric scenes, which is used to generate training data for
learning depth-based pose classifiers. We evaluate our approach on an annotated
dataset of real egocentric object manipulation scenes and compare to both
commercial and academic approaches. Our method provides state-of-the-art
performance for both hand detection and pose estimation in egocentric RGB-D
images.
