MotionAGFormer: Enhancing 3D Human Pose Estimation with a
  Transformer-GCNFormer Network
Soroush Mehraban Vida Adeli Babak Taati
Recent transformer-based approaches have demonstrated excellent performance
in 3D human pose estimation. However, they have a holistic view and by encoding
global relationships between all the joints, they do not capture the local
dependencies precisely. In this paper, we present a novel Attention-GCNFormer
(AGFormer) block that divides the number of channels by using two parallel
transformer and GCNFormer streams. Our proposed GCNFormer module exploits the
local relationship between adjacent joints, outputting a new representation
that is complementary to the transformer output. By fusing these two
representation in an adaptive way, AGFormer exhibits the ability to better
learn the underlying 3D structure. By stacking multiple AGFormer blocks, we
propose MotionAGFormer in four different variants, which can be chosen based on
the speed-accuracy trade-off. We evaluate our model on two popular benchmark
datasets: Human3.6M and MPI-INF-3DHP. MotionAGFormer-B achieves
state-of-the-art results, with P1 errors of 38.4mm and 16.2mm, respectively.
Remarkably, it uses a quarter of the parameters and is three times more
computationally efficient than the previous leading model on Human3.6M dataset.
Code and models are available at https://github.com/TaatiTeam/MotionAGFormer.