  In recent years, 2D-to-3D pose uplifting in monocular 3D Human Pose
Estimation (HPE) has attracted widespread research interest. GNN-based methods
and Transformer-based methods have become mainstream architectures due to their
advanced spatial and temporal feature learning capacities. However, existing
approaches typically construct joint-wise and frame-wise attention alignments
in spatial and temporal domains, resulting in dense connections that introduce
considerable local redundancy and computational overhead. In this paper, we
take a global approach to exploit spatio-temporal information and realise
efficient 3D HPE with a concise Graph and Skipped Transformer architecture.
Specifically, in Spatial Encoding stage, coarse-grained body parts are deployed
to construct Spatial Graph Network with a fully data-driven adaptive topology,
ensuring model flexibility and generalizability across various poses. In
Temporal Encoding and Decoding stages, a simple yet effective Skipped
Transformer is proposed to capture long-range temporal dependencies and
implement hierarchical feature aggregation. A straightforward Data Rolling
strategy is also developed to introduce dynamic information into 2D pose
sequence. Extensive experiments are conducted on Human3.6M, MPI-INF-3DHP and
Human-Eva benchmarks. G-SFormer series methods achieve superior performances
compared with previous state-of-the-arts with only around ten percent of
parameters and significantly reduced computational complexity. Additionally,
G-SFormer also exhibits outstanding robustness to inaccuracies in detected 2D
poses.
