Name
Abstract | ||
|---|---|---|
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 base-line 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.01985 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Face and gestures, 3D from multi-view and sensors, 3D from single images, Datasets and evaluation, Motion and tracking | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jialian Li | 1 | 0 | 0.34 |
| Jingyi Zhang | 2 | 0 | 0.34 |
| Zhiyong Wang | 3 | 0 | 0.34 |
| Siqi Shen | 4 | 0 | 0.68 |
| Chenglu Wen | 5 | 121 | 19.17 |
| Yuexin Ma | 6 | 0 | 0.34 |
| Lan Xu | 7 | 30 | 11.01 |
| Jingyi Yu | 8 | 1238 | 101.25 |
| Cheng Wang | 9 | 118 | 29.56 |