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.

提出了第一个直接的端到端多人姿态估计框架，称为 DirectPose，使用新的关键点对齐机制 (KPAlign)，该机制提高了准确性，并消除了需在底部向上方法中处理的启发式分组或在自顶向下方法中进行边界框检测和 RoI 操作。实验证明，这一端到端模式在人体姿态估计任务中可以达到与之前强大基线相媲美或更好的表现。