While CNN-based models have made remarkable progress on human pose
estimation, what spatial dependencies they capture to localize keypoints
remains unclear. In this work, we propose a model called \textbf{TransPose},
which introduces Transformer for human pose estimation. The attention layers
built in Transformer enable our model to capture long-range relationships
efficiently and also can reveal what dependencies the predicted keypoints rely
on. To predict keypoint heatmaps, the last attention layer acts as an
aggregator, which collects contributions from image clues and forms maximum
positions of keypoints. Such a heatmap-based localization approach via
Transformer conforms to the principle of Activation
Maximization~\cite{erhan2009visualizing}. And the revealed dependencies are
image-specific and fine-grained, which also can provide evidence of how the
model handles special cases, e.g., occlusion. The experiments show that
TransPose achieves 75.8 AP and 75.0 AP on COCO validation and test-dev sets,
while being more lightweight and faster than mainstream CNN architectures. The
TransPose model also transfers very well on MPII benchmark, achieving superior
performance on the test set when fine-tuned with small training costs. Code and
pre-trained models are publicly
available\footnote{https://github.com/yangsenius/TransPose}.

本文介绍了一种名为 TransPose 的模型，它是用 Transformer 来进行人体姿态估计的，能够有效地捕捉长距离的关系以及细粒度的图像依赖关系，并通过产生热力图来进行关键点的定位。基于此，模型可以实现更快速的处理速度和更轻量化的模型结构。