  Existing motion capture datasets are largely short-range and cannot yet fit
the need of long-range applications. We propose LiDARHuman26M, a new human
motion capture dataset captured by LiDAR at a much longer range to overcome
this limitation. Our dataset also includes the ground truth human motions
acquired by the IMU system and the synchronous RGB images. We further present a
strong baseline method, LiDARCap, for LiDAR point cloud human motion capture.
Specifically, we first utilize PointNet++ to encode features of points and then
employ the inverse kinematics solver and SMPL optimizer to regress the pose
through aggregating the temporally encoded features hierarchically.
Quantitative and qualitative experiments show that our method outperforms the
techniques based only on RGB images. Ablation experiments demonstrate that our
dataset is challenging and worthy of further research. Finally, the experiments
on the KITTI Dataset and the Waymo Open Dataset show that our method can be
generalized to different LiDAR sensor settings.
