LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds
This work addresses the need for long-range motion capture in applications like autonomous driving, though it is incremental as it builds on existing datasets and methods.
The authors tackled the lack of long-range motion capture data by introducing LiDARHuman26M, a dataset captured with LiDAR at extended ranges, and proposed LiDARCap, a baseline method that outperforms RGB-based techniques in 3D human motion capture.
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.