  Multi-person pose estimation is a fundamental and challenging problem to many
computer vision tasks. Most existing methods can be broadly categorized into
two classes: top-down and bottom-up methods. Both of the two types of methods
involve two stages, namely, person detection and joints detection.
Conventionally, the two stages are implemented separately without considering
their interactions between them, and this may inevitably cause some issue
intrinsically. In this paper, we present a novel method to simplify the
pipeline by implementing person detection and joints detection simultaneously.
We propose a Double Embedding (DE) method to complete the multi-person pose
estimation task in a global-to-local way. DE consists of Global Embedding (GE)
and Local Embedding (LE). GE encodes different person instances and processes
information covering the whole image and LE encodes the local limbs
information. GE functions for the person detection in top-down strategy while
LE connects the rest joints sequentially which functions for joint grouping and
information processing in A bottom-up strategy. Based on LE, we design the
Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex
scenarios. MRM can effectively realize the information communicating between
keypoints and further improve the accuracy. We achieve the competitive results
on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and
generalization ability of our method.
