  We tackle the problem of estimating the 3D pose of an individual's upper
limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider
pose estimation during everyday interactions with objects. Past work shows that
strong pose+viewpoint priors and depth-based features are crucial for robust
performance. In egocentric views, hands and arms are observable within a well
defined volume in front of the camera. We call this volume an egocentric
workspace. A notable property is that hand appearance correlates with workspace
location. To exploit this correlation, we classify arm+hand configurations in a
global egocentric coordinate frame, rather than a local scanning window. This
greatly simplify the architecture and improves performance. We propose an
efficient pipeline which 1) generates synthetic workspace exemplars for
training using a virtual chest-mounted camera whose intrinsic parameters match
our physical camera, 2) computes perspective-aware depth features on this
entire volume and 3) recognizes discrete arm+hand pose classes through a sparse
multi-class SVM. Our method provides state-of-the-art hand pose recognition
performance from egocentric RGB-D images in real-time.
