The proposed LiDARHuman26M benchmark dataset consists of synchronous LiDAR point clouds, RGB images, and ground-truth 3D human motions obtained from professional IMU devices, covering diverse motions and a large capture distance ranging. Based on LiDARHuman26M, we propose LiDARCap, a strong baseline motion capture approach on LiDAR point clouds, which achieves promising results as shown on the right end.


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.



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|── images
|   |── 5
|   |   |── 000001.png
|   |   |── 000002.png
|   |   |── ...
|   |── ...
|   |── 42
|── labels/3d
    |── segment
    |   |── 5
    |   |   |── 000001.ply
    |   |   |── 000002.ply
    |   |   |── ...
    |   |── ...
    |   |── 42
    |── pose
        |── 5
        |   |── 000001.json
        |   |── 000002.json
        |   |── ...
        |── ...
        |── 42


  1. Point clouds that contains only the volunteers are stored in lidarhuman26M/labels/3d/segment
  2. The ground truth 3D human motions are provided in the form of SMPL parameters(pose, shape and trans) in lidarhuman26M/labels/3d/pose. If you want to generate the corresponding mesh, you can use the smplx, a Python module whose specification can be found here.
  3. Because of the limited space, we only provide the volunteer part of the images in png format in lidarhuman26M/images. If you want to project the point clouds onto the images, you can use the code below.
from plyfile import PlyData

import json
import numpy as np
import os
import torch

def affine(X, matrix):
    n = X.shape[0]
    if type(X) == np.ndarray:
        res = np.concatenate((X, np.ones((n, 1))), axis=-1).T
        res =, res).T
        res =, torch.ones((n, 1)).to(X.device)), axis=-1)
        res =
    return res[..., :-1]

def lidar_to_camera(X, extrinsic_matrix):
    return affine(X, extrinsic_matrix)

def camera_to_pixel(X, intrinsic_matrix, distortion_coefficients):
    # focal length
    f = np.array([intrinsic_matrix[0, 0], intrinsic_matrix[1, 1]])
    # center principal point
    c = np.array([intrinsic_matrix[0, 2], intrinsic_matrix[1, 2]])
    k = np.array([distortion_coefficients[0],
                 distortion_coefficients[1], distortion_coefficients[4]])
    p = np.array([distortion_coefficients[2], distortion_coefficients[3]])
    XX = X[..., :2] / X[..., 2:]
    r2 = np.sum(XX[..., :2]**2, axis=-1, keepdims=True)

    radial = 1 + np.sum(k * np.concatenate((r2, r2**2, r2**3),
                        axis=-1), axis=-1, keepdims=True)

    tan = 2 * np.sum(p * XX[..., ::-1], axis=-1, keepdims=True)
    XXX = XX * (radial + tan) + r2 * p[..., ::-1]
    return f * XXX + c

def read_point_cloud(filename):
    """ read XYZ point cloud from filename PLY file """
    ply_data =['vertex'].data
    points = np.array([[x, y, z] for x, y, z in ply_data])
    return points

def project_points_on_segment_image(index):

    extrinsic_matrix = np.array([-0.0043368991524, -0.99998911867, -0.0017186757713, 0.016471385748, -0.0052925495236, 0.0017416212982, -
                                0.99998447772, 0.080050847871, 0.99997658984, -0.0043277356572, -0.0053000451695, -0.049279053295, 0, 0, 0, 1]).reshape(4, 4)
    intrinsic_matrix = np.array([9.5632709662202160e+02, 0., 9.6209910493679433e+02,
                                0., 9.5687763573729683e+02, 5.9026610775785059e+02, 0., 0., 1.]).reshape(3, 3)
    distortion_coefficients = np.array([-6.1100617222502205e-03, 3.0647823796371827e-02, -
                                        3.3304524444662654e-04, -4.4038460096976607e-04, -2.5974982760794661e-02])

    dataset_folder = '/path/to/lidarhuman26M'
    with open(os.path.join(dataset_folder, 'lidarhuman26M_top_left.json')) as f:
        data = json.load(f)

    image_filename = os.path.join(
        dataset_folder, 'images/{}.png'.format(index))
    point_cloud_filename = os.path.join(
        dataset_folder, 'labels/3d/segment/{}.ply'.format(index))
    point_cloud = read_point_cloud(point_cloud_filename)
    points_on_image = camera_to_pixel(lidar_to_camera(
        point_cloud, extrinsic_matrix), intrinsic_matrix, distortion_coefficients)
    top_left_coord = np.array(data[index])
    points_on_image -= top_left_coord
    return points_on_image

if __name__ == '__main__':


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  title={LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds},
  author={Li, Jialian and Zhang, Jingyi and Wang, Zhiyong and Shen, Siqi and Wen, Chenglu and Ma, Yuexin and Xu, Lan and Yu, Jingyi and Wang, Cheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

Further information and commercial licensing

For further information, or for commercial licensing, please contact us at the following email address:


  title={LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds},
  author={Li, Jialian and Zhang, Jingyi and Wang, Zhiyong and Shen, Siqi and Wen, Chenglu and Ma, Yuexin and Xu, Lan and Yu, Jingyi and Wang, Cheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},