  Human-centric Point Cloud Video Understanding (PVU) is an emerging field
focused on extracting and interpreting human-related features from sequences of
human point clouds, further advancing downstream human-centric tasks and
applications. Previous works usually focus on tackling one specific task and
rely on huge labeled data, which has poor generalization capability.
Considering that human has specific characteristics, including the structural
semantics of human body and the dynamics of human motions, we propose a unified
framework to make full use of the prior knowledge and explore the inherent
features in the data itself for generalized human-centric point cloud video
understanding. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on various human-related tasks, including action
recognition and 3D pose estimation. All datasets and code will be released
soon.
