标题： 学习金字塔结构的长程依赖关系用于三维人体姿态估计 显示英文标题

标题： Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation

摘要： 人体结构中的动作协调对于恢复3D姿势的二维关节的空间约束至关重要。 通常，动作协调被表示为身体部位之间的长距离依赖关系。 然而，在建模长距离依赖关系时存在两个主要挑战。 首先，关节不仅应受到其他单个关节的约束，还应受到身体部位的调制。 其次，现有方法通过使网络更深来学习非连接部分之间的依赖关系。 它们引入了不相关的噪声并增加了模型规模。 在本文中，我们利用金字塔结构更好地学习潜在的长距离依赖关系。 它可以捕获关节和组之间的相关性，从而补充人体子结构的上下文。 以一种有效的跨尺度方式，它捕获了金字塔结构的长距离依赖关系。 具体而言，我们提出了一个新颖的金字塔图注意力（PGA）模块来捕获长距离跨尺度依赖关系。 它将来自不同尺度的信息拼接成一个紧凑的序列，然后并行计算尺度之间的相关性。 结合PGA与图卷积模块，我们开发了一种用于3D人体姿态估计的金字塔图Transformer（PGFormer），这是一种轻量级多尺度Transformer架构。 它通过池化将人体子结构封装到自注意力中。 大量的实验表明，我们的方法在Human3.6M和MPI-INF-3DHP数据集上的误差更低且模型更小。 代码可在https://github.com/MingjieWe/PGFormer获取。 显示英文摘要

摘要： Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to better learn potential long-range dependencies. It can capture the correlation across joints and groups, which complements the context of the human sub-structure. In an effective cross-scale way, it captures the pyramid-structured long-range dependence. Specifically, we propose a novel Pyramid Graph Attention (PGA) module to capture long-range cross-scale dependencies. It concatenates information from various scales into a compact sequence, and then computes the correlation between scales in parallel. Combining PGA with graph convolution modules, we develop a Pyramid Graph Transformer (PGFormer) for 3D human pose estimation, which is a lightweight multi-scale transformer architecture. It encapsulates human sub-structures into self-attention by pooling. Extensive experiments show that our approach achieves lower error and smaller model size than state-of-the-art methods on Human3.6M and MPI-INF-3DHP datasets. The code is available at https://github.com/MingjieWe/PGFormer.