We present a method for learning an embedding that places images of humans in
similar poses nearby. This embedding can be used as a direct method of comparing
images based on human pose, avoiding potential challenges of estimating body joint
positions. Pose embedding learning is formulated under a triplet-based distance
criterion. A deep architecture is used to allow learning of a representation
capable of making distinctions between different poses. Experiments on human pose
matching and retrieval from video data demonstrate the potential of the method.