  Human pose estimation aims to figure out the keypoints of all people in
different scenes. Current approaches still face some challenges despite
promising results. Existing top-down methods deal with a single person
individually, without the interaction between different people and the scene
they are situated in. Consequently, the performance of human detection degrades
when serious occlusion happens. On the other hand, existing bottom-up methods
consider all people at the same time and capture the global knowledge of the
entire image. However, they are less accurate than the top-down methods due to
the scale variation. To address these problems, we propose a novel
Dual-Pipeline Integrated Transformer (DPIT) by integrating top-down and
bottom-up pipelines to explore the visual clues of different receptive fields
and achieve their complementarity. Specifically, DPIT consists of two branches,
the bottom-up branch deals with the whole image to capture the global visual
information, while the top-down branch extracts the feature representation of
local vision from the single-human bounding box. Then, the extracted feature
representations from bottom-up and top-down branches are fed into the
transformer encoder to fuse the global and local knowledge interactively.
Moreover, we define the keypoint queries to explore both full-scene and
single-human posture visual clues to realize the mutual complementarity of the
two pipelines. To the best of our knowledge, this is one of the first works to
integrate the bottom-up and top-down pipelines with transformers for human pose
estimation. Extensive experiments on COCO and MPII datasets demonstrate that
our DPIT achieves comparable performance to the state-of-the-art methods.
