  We introduce CenterGroup, an attention-based framework to estimate human
poses from a set of identity-agnostic keypoints and person center predictions
in an image. Our approach uses a transformer to obtain context-aware embeddings
for all detected keypoints and centers and then applies multi-head attention to
directly group joints into their corresponding person centers. While most
bottom-up methods rely on non-learnable clustering at inference, CenterGroup
uses a fully differentiable attention mechanism that we train end-to-end
together with our keypoint detector. As a result, our method obtains
state-of-the-art performance with up to 2.5x faster inference time than
competing bottom-up methods. Our code is available at
https://github.com/dvl-tum/center-group .
