  LiDAR-based human motion capture has garnered significant interest in recent
years for its practicability in large-scale and unconstrained environments.
However, most methods rely on cleanly segmented human point clouds as input,
the accuracy and smoothness of their motion results are compromised when faced
with noisy data, rendering them unsuitable for practical applications. To
address these limitations and enhance the robustness and precision of motion
capture with noise interference, we introduce LiveHPS++, an innovative and
effective solution based on a single LiDAR system. Benefiting from three
meticulously designed modules, our method can learn dynamic and kinematic
features from human movements, and further enable the precise capture of
coherent human motions in open settings, making it highly applicable to
real-world scenarios. Through extensive experiments, LiveHPS++ has proven to
significantly surpass existing state-of-the-art methods across various
datasets, establishing a new benchmark in the field.
