  We propose the first direct end-to-end multi-person pose estimation
framework, termed DirectPose. Inspired by recent anchor-free object detectors,
which directly regress the two corners of target bounding-boxes, the proposed
framework directly predicts instance-aware keypoints for all the instances from
a raw input image, eliminating the need for heuristic grouping in bottom-up
methods or bounding-box detection and RoI operations in top-down ones. We also
propose a novel Keypoint Alignment (KPAlign) mechanism, which overcomes the
main difficulty: lack of the alignment between the convolutional features and
predictions in this end-to-end framework. KPAlign improves the framework's
performance by a large margin while still keeping the framework end-to-end
trainable. With the only postprocessing non-maximum suppression (NMS), our
proposed framework can detect multi-person keypoints with or without
bounding-boxes in a single shot. Experiments demonstrate that the end-to-end
paradigm can achieve competitive or better performance than previous strong
baselines, in both bottom-up and top-down methods. We hope that our end-to-end
approach can provide a new perspective for the human pose estimation task.
