  2D-to-3D human pose lifting is fundamental for 3D human pose estimation
(HPE), for which graph convolutional networks (GCNs) have proven inherently
suitable for modeling the human skeletal topology. However, the current
GCN-based 3D HPE methods update the node features by aggregating their
neighbors' information without considering the interaction of joints in
different joint synergies. Although some studies have proposed importing limb
information to learn the movement patterns, the latent synergies among joints,
such as maintaining balance are seldom investigated. We propose the Hop-wise
GraphFormer with Intragroup Joint Refinement (HopFIR) architecture to tackle
the 3D HPE problem. HopFIR mainly consists of a novel hop-wise GraphFormer
(HGF) module and an intragroup joint refinement (IJR) module. The HGF module
groups the joints by k-hop neighbors and applies a hopwise transformer-like
attention mechanism to these groups to discover latent joint synergies. The IJR
module leverages the prior limb information for peripheral joint refinement.
Extensive experimental results show that HopFIR outperforms the SOTA methods by
a large margin, with a mean per-joint position error (MPJPE) on the Human3.6M
dataset of 32.67 mm. We also demonstrate that the state-of-the-art GCN-based
methods can benefit from the proposed hop-wise attention mechanism with a
significant improvement in performance: SemGCN and MGCN are improved by 8.9%
and 4.5%, respectively.
