  The model-based gait recognition methods usually adopt the pedestrian walking
postures to identify human beings.
  However, existing methods did not explicitly resolve the large intra-class
variance of human pose due to camera views changing.
  In this paper, we propose to generate multi-view pose sequences for each
single-view pose sample by learning full-rank transformation matrices via
lower-upper generative adversarial network (LUGAN).
  By the prior of camera imaging, we derive that the spatial coordinates
between cross-view poses satisfy a linear transformation of a full-rank matrix,
thereby, this paper employs the adversarial training to learn transformation
matrices from the source pose and target views to obtain the target pose
sequences.
  To this end, we implement a generator composed of graph convolutional (GCN)
layers, fully connected (FC) layers and two-branch convolutional (CNN) layers:
GCN layers and FC layers encode the source pose sequence and target view, then
CNN branches learn a lower triangular matrix and an upper triangular matrix,
respectively, finally they are multiplied to formulate the full-rank
transformation matrix.
  For the purpose of adversarial training, we further devise a condition
discriminator that distinguishes whether the pose sequence is true or
generated.
  To enable the high-level correlation learning, we propose a plug-and-play
module, named multi-scale hypergraph convolution (HGC), to replace the spatial
graph convolutional layer in baseline, which could simultaneously model the
joint-level, part-level and body-level correlations.
  Extensive experiments on two large gait recognition datasets, i.e., CASIA-B
and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and
existing pose-based methods by a large margin.
