  The practical application requests both accuracy and efficiency on
multi-person pose estimation algorithms. But the high accuracy and fast
inference speed are dominated by top-down methods and bottom-up methods
respectively. To make a better trade-off between accuracy and efficiency, we
propose a novel multi-person pose estimation framework, SIngle-network with
Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE).
Specifically, in the training process, we enable SIMPLE to mimic the pose
knowledge from the high-performance top-down pipeline, which significantly
promotes SIMPLE's accuracy while maintaining its high efficiency during
inference. Besides, SIMPLE formulates human detection and pose estimation as a
unified point learning framework to complement each other in single-network.
This is quite different from previous works where the two tasks may interfere
with each other. To the best of our knowledge, both mimicking strategy between
different method types and unified point learning are firstly proposed in pose
estimation. In experiments, our approach achieves the new state-of-the-art
performance among bottom-up methods on the COCO, MPII and PoseTrack datasets.
Compared with the top-down approaches, SIMPLE has comparable accuracy and
faster inference speed.
