Human pose estimation is a complicated structured data sequence modeling
task. Most existing methods only consider the pair-wise interaction of human
body joints in model learning. Unfortunately, this causes 3D pose estimation to
fail in difficult cases such as $\textit{joints overlapping}$, and pose
$\textit{fast-changing}$, as pair-wise relations cannot exploit fine-grained
human body priors in pose estimation. To this end, we revamped the 3D pose
estimation framework with a $\textit{High-order}$ $\textit{Directed}$
$\textit{Transformer}$ (HDFormer), which coherently exploits the high-order
bones and joints relevances to boost the performance of pose estimation.
Specifically, HDFormer adopts both self-attention and high-order attention
schemes to build up a multi-order attention module to perform the information
flow interaction including the first-order
$"\textit{joint$\leftrightarrow$joint}"$, second-order
$"\textit{bone$\leftrightarrow$joint}"$ as well as high-order
$"\textit{hyperbone$\leftrightarrow$joint}"$ relationships (hyperbone is
defined as a joint set), compensating the hard cases prediction in
fast-changing and heavy occlusion scenarios. Moreover, modernized CNN
techniques are applied to upgrade the transformer-based architecture to speed
up the HDFormer, achieving a favorable trade-off between effectiveness and
efficiency. We compare our model with other SOTA models on the datasets
Human3.6M and MPI-INF-3DHP. The results demonstrate that the proposed HDFormer
achieves superior performance with only $\textbf{1/10}$ parameters and much
lower computational cost compared to the current SOTAs. Moreover, HDFormer can
be applied to various types of real-world applications, enabling real-time and
accurate 3D pose estimation. The source code is in
this https URL.

本文提出了使用多阶注意力模块与高阶关节关联的高阶有向变换器（HDFormer）进行人体姿势估计的新框架，该框架可以利用细粒度的人体先验知识来弥补现有方法在处理关节点重叠和快速变化时的不足，实现了实时准确的 3D 姿态估计。