  Human motion prediction (HMP) has emerged as a popular research topic due to
its diverse applications, but it remains a challenging task due to the
stochastic and aperiodic nature of future poses. Traditional methods rely on
hand-crafted features and machine learning techniques, which often struggle to
model the complex dynamics of human motion. Recent deep learning-based methods
have achieved success by learning spatio-temporal representations of motion,
but these models often overlook the reliability of motion data. Additionally,
the temporal and spatial dependencies of skeleton nodes are distinct. The
temporal relationship captures motion information over time, while the spatial
relationship describes body structure and the relationships between different
nodes. In this paper, we propose a novel spatio-temporal branching network
using incremental information for HMP, which decouples the learning of
temporal-domain and spatial-domain features, extracts more motion information,
and achieves complementary cross-domain knowledge learning through knowledge
distillation. Our approach effectively reduces noise interference and provides
more expressive information for characterizing motion by separately extracting
temporal and spatial features. We evaluate our approach on standard HMP
benchmarks and outperform state-of-the-art methods in terms of prediction
accuracy.
