  Despite substantial progress in 3D human pose estimation from a single-view
image, prior works rarely explore global and local correlations, leading to
insufficient learning of human skeleton representations. To address this issue,
we propose a novel Interweaved Graph and Attention Network (IGANet) that allows
bidirectional communications between graph convolutional networks (GCNs) and
attentions. Specifically, we introduce an IGA module, where attentions are
provided with local information from GCNs and GCNs are injected with global
information from attentions. Additionally, we design a simple yet effective
U-shaped multi-layer perceptron (uMLP), which can capture multi-granularity
information for body joints. Extensive experiments on two popular benchmark
datasets (i.e. Human3.6M and MPI-INF-3DHP) are conducted to evaluate our
proposed method.The results show that IGANet achieves state-of-the-art
performance on both datasets. Code is available at
https://github.com/xiu-cs/IGANet.
