  Graph convolutional networks have been widely applied in skeleton-based gait
recognition. A key challenge in this task is to distinguish the individual
walking styles of different subjects across various views. Existing
state-of-the-art methods employ uniform convolutions to extract features from
diverse sequences and ignore the effects of viewpoint changes. To overcome
these limitations, we propose a condition-adaptive graph (CAG) convolution
network that can dynamically adapt to the specific attributes of each skeleton
sequence and the corresponding view angle. In contrast to using fixed weights
for all joints and sequences, we introduce a joint-specific filter learning
(JSFL) module in the CAG method, which produces sequence-adaptive filters at
the joint level. The adaptive filters capture fine-grained patterns that are
unique to each joint, enabling the extraction of diverse spatial-temporal
information about body parts. Additionally, we design a view-adaptive topology
learning (VATL) module that generates adaptive graph topologies. These graph
topologies are used to correlate the joints adaptively according to the
specific view conditions. Thus, CAG can simultaneously adjust to various
walking styles and viewpoints. Experiments on the two most widely used datasets
(i.e., CASIA-B and OU-MVLP) show that CAG surpasses all previous skeleton-based
methods. Moreover, the recognition performance can be enhanced by simply
combining CAG with appearance-based methods, demonstrating the ability of CAG
to provide useful complementary information.The source code will be available
at https://github.com/OliverHxh/CAG.
