SpATr: MoCap 3D Human Action Recognition based on Spiral Auto-encoder
  and Transformer Network
Hamza Bouzid Lahoucine Ballihi
Recent advancements in technology have expanded the possibilities of human
action recognition by leveraging 3D data, which offers a richer representation
of actions through the inclusion of depth information, enabling more accurate
analysis of spatial and temporal characteristics. However, 3D human action
recognition is a challenging task due to the irregularity and Disarrangement of
the data points in action sequences. In this context, we present our novel
model for human action recognition from fixed topology mesh sequences based on
Spiral Auto-encoder and Transformer Network, namely SpATr. The proposed method
first disentangles space and time in the mesh sequences. Then, an auto-encoder
is utilized to extract spatial geometrical features, and tiny transformer is
used to capture the temporal evolution of the sequence. Previous methods either
use 2D depth images, sample skeletons points or they require a huge amount of
memory leading to the ability to process short sequences only. In this work, we
show competitive recognition rate and high memory efficiency by building our
auto-encoder based on spiral convolutions, which are light weight convolution
directly applied to mesh data with fixed topologies, and by modeling temporal
evolution using a attention, that can handle large sequences. The proposed
method is evaluated on on two 3D human action datasets: MoVi and BMLrub from
the Archive of Motion Capture As Surface Shapes (AMASS). The results analysis
shows the effectiveness of our method in 3D human action recognition while
maintaining high memory efficiency. The code will soon be made publicly
available.