  We present a fast bottom-up method that jointly detects over 100 keypoints on
humans or objects, also referred to as human/object pose estimation. We model
all keypoints belonging to a human or an object -- the pose -- as a graph and
leverage insights from community detection to quantify the independence of
keypoints. We use a graph centrality measure to assign training weights to
different parts of a pose. Our proposed measure quantifies how tightly a
keypoint is connected to its neighborhood. Our experiments show that our method
outperforms all previous methods for human pose estimation with fine-grained
keypoint annotations on the face, the hands and the feet with a total of 133
keypoints. We also show that our method generalizes to car poses.
