Previously, 3-4 various algorithms were needed to do that.
Duncan Zauss, Sven Kreiss, and Alexandre Alahi presented Keypoint Communities: a fast method that jointly detects over 100 keypoints on humans or objects. This neural network can detect human poses, facial expressions, finger movement, and more from a single image. Previously, several networks were needed to get the same amount of information, while Keypoint Communities does everything on its own.
"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," comments the team behind Keypoint Communities. "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."
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