  For human-centric large-scale scenes, fine-grained modeling for 3D human
global pose and shape is significant for scene understanding and can benefit
many real-world applications. In this paper, we present LiveHPS, a novel
single-LiDAR-based approach for scene-level human pose and shape estimation
without any limitation of light conditions and wearable devices. In particular,
we design a distillation mechanism to mitigate the distribution-varying effect
of LiDAR point clouds and exploit the temporal-spatial geometric and dynamic
information existing in consecutive frames to solve the occlusion and noise
disturbance. LiveHPS, with its efficient configuration and high-quality output,
is well-suited for real-world applications. Moreover, we propose a huge human
motion dataset, named FreeMotion, which is collected in various scenarios with
diverse human poses, shapes and translations. It consists of multi-modal and
multi-view acquisition data from calibrated and synchronized LiDARs, cameras,
and IMUs. Extensive experiments on our new dataset and other public datasets
demonstrate the SOTA performance and robustness of our approach. We will
release our code and dataset soon.
