  Human pose is typically represented by a coordinate vector of body joints or
their heatmap embeddings. While easy for data processing, unrealistic pose
estimates are admitted due to the lack of dependency modeling between the body
joints. In this paper, we present a structured representation, named Pose as
Compositional Tokens (PCT), to explore the joint dependency. It represents a
pose by M discrete tokens with each characterizing a sub-structure with several
interdependent joints. The compositional design enables it to achieve a small
reconstruction error at a low cost. Then we cast pose estimation as a
classification task. In particular, we learn a classifier to predict the
categories of the M tokens from an image. A pre-learned decoder network is used
to recover the pose from the tokens without further post-processing. We show
that it achieves better or comparable pose estimation results as the existing
methods in general scenarios, yet continues to work well when occlusion occurs,
which is ubiquitous in practice. The code and models are publicly available at
https://github.com/Gengzigang/PCT.
