  We propose a unified framework for multi-person pose estimation and tracking.
Our framework consists of two main components,~\ie~SpatialNet and TemporalNet.
The SpatialNet accomplishes body part detection and part-level data association
in a single frame, while the TemporalNet groups human instances in consecutive
frames into trajectories. Specifically, besides body part detection heatmaps,
SpatialNet also predicts the Keypoint Embedding (KE) and Spatial Instance
Embedding (SIE) for body part association. We model the grouping procedure into
a differentiable Pose-Guided Grouping (PGG) module to make the whole part
detection and grouping pipeline fully end-to-end trainable. TemporalNet extends
spatial grouping of keypoints to temporal grouping of human instances. Given
human proposals from two consecutive frames, TemporalNet exploits both
appearance features encoded in Human Embedding (HE) and temporally consistent
geometric features embodied in Temporal Instance Embedding (TIE) for robust
tracking. Extensive experiments demonstrate the effectiveness of our proposed
model. Remarkably, we demonstrate substantial improvements over the
state-of-the-art pose tracking method from 65.4\% to 71.8\% Multi-Object
Tracking Accuracy (MOTA) on the ICCV'17 PoseTrack Dataset.
