Keypoint Communities

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. 2021: Duncan Zauss, S. Kreiss, Alexandre Alahi Ranked #1 on Car Pose Estimation on ApolloCar3D https://arxiv.org/pdf/2110.00988v1.pdf