  We propose a novel attention-based 2D-to-3D pose estimation network for
graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation
network for hand data, named GASE-Net. Previous 3D pose estimation methods have
focused on various modifications to the graph convolution kernel, such as
abandoning weight sharing or increasing the receptive field. Some of these
methods employ attention-based non-local modules as auxiliary modules. In order
to better model the relationship between nodes in graph-structured data and
fuse the information of different neighbor nodes in a differentiated way, we
make targeted modifications to the attention module and propose two modules
designed for graph-structured data, graph relative positional encoding
multi-head self-attention (GR-MSA) and K-order graph-oriented multi-head
self-attention (KOG-MSA). By stacking GR-MSA and KOG-MSA, we propose a novel
network KOG-Transformer for 2D-to-3D pose estimation. Furthermore, we propose a
network for shape estimation on hand data, called GraAttention shape estimation
network (GASE-Net), which takes a 3D pose as input and gradually models the
shape of the hand from sparse to dense. We have empirically shown the
superiority of KOG-Transformer through extensive experiments. Experimental
results show that KOG-Transformer significantly outperforms the previous
state-of-the-art methods on the benchmark dataset Human3.6M. We evaluate the
effect of GASE-Net on two public available hand datasets, ObMan and
InterHand2.6M. GASE-Net can predict the corresponding shape for input pose with
strong generalization ability.
