Capturing the motion of humans without any markers in a moving scene is a challenging issue. Typical methods, such as those based on fixed cameras or wearable sensors, have limits on the measurement range and the number of targets. Therefore, we propose a markerless motion capture technique to estimate the 3D pose of humans through a multi-UAV system. By utilizing the advantage of the large observation baselines between UAVs in the system, an algorithm to estimate the pose of airborne cameras and establish the sparse points cloud of background in overlapping view simultaneously was presented. A 3σ rule based on reprojection error is employed to eliminate the incorrect conjugate keypoint pairs, enhancing the precision of pose estimation for airborne cameras. To verify the accuracy of the developed technique, an indoor test was conducted to compare it with a commercial capture system, the results show that the average measurement error of the 3D joints of humans is less than 30mm. Furthermore, we demonstrate the feasibility of our approach in outdoor scenes, including multi-person scenarios and moving scenarios.

The multi‐camera calibration is an essential step for many spatially aware applications, such as robotic navigation, augmented reality, and 3D human pose estimation. Traditional calibration methods use off‐the‐shelf checkerboards or triangles as the known world coordinate system and their corresponding corners are set as control points, which heavily depends on specific calibration patterns and is not suitable for calibration pattern‐denied environments. In this paper, an automatic calibration method is proposed to calibrate the multi‐camera system without the aid of a known calibration pattern. The key idea of the proposed method is that the authors consider the human body, which is always available, as the counterpart of the calibration pattern. The authors’ approach starts with binocular camera calibration, in which the extrinsic and intrinsic parameters are calculated in order and followed by a joint optimisation. With the results of each pair of binocular camera calibration, the multi‐camera system calibration is carried out in three steps: (i) parameters initialisation, (ii) extrinsic parameters optimisation, and (iii) jointly optimising intrinsic and extrinsic parameters. Since the authors’ approach does not require additional calibration patterns except for one visible person, it is flexible and easy to be implemented. Real experiments are conducted in different scenes, camera angles, and camera settings. Human pose estimation with the multi‐camera system is additionally performed for exhaustive experiments. The experimental results demonstrate that the authors’ method shows superior performance than the traditional method with the aid of a specific calibration pattern.

Space exploration missions involve significant participation from astronauts. Therefore, it is of great practical importance to assess the astronauts’ performance via various parameters in the cramped and weightless space station. In this paper, we proposed a calibration-free multi-view vision system for astronaut performance capture, including two modules: (1) an alternating iterative optimization of the camera pose and human pose is implemented to calibrate the extrinsic camera parameters with detected 2D keypoints. (2) Scale factors are restricted by the limb length to recover the real-world scale and the shape parameters are refined for subsequent postural reconstruction. These two modules can provide effective and efficient motion capture in a weightless space station. Extensive experiments using public datasets and the ground verification test data demonstrated the accuracy of the estimated camera pose and the effectiveness of the reconstructed human pose.

For a multi-mode Earth observation satellite carrying a line array camera and a multi-beam line array LiDAR, the relative installation attitude of the two sensors is of great significance. In this paper, we propose an on-orbit calibration method for the relative installation attitude of the camera and the LiDAR with no need for the calibration field and additional satellite attitude maneuvers. Firstly, the on-orbit joint calibration model of the relative installation attitude of the two sensors is established. However, there may exist a multi-solution problem in the solving of the above model constrained by non-ground control points. Thus, an alternate iterative method by solving the pseudo-absolute attitude matrix of each sensor in turn is proposed. The numerical validation and simulation experiments results show that the relative positioning error of the line array camera and the LiDAR in the horizontal direction of the ground can be limited to 0.8 m after correction by the method in this paper.

Transformer-based methods have recently achieved significant success in 3D human pose estimation, owing to their strong ability to model long-range dependencies. However, relying solely on the global attention mechanism is insufficient for capturing the fine-grained local details, which are crucial for accurate pose estimation. Existing local feature extraction networks, such as Graph Convolutional Networks, often suffer from over-smoothing, while small-kernel CNNs have limited receptive fields and are highly sensitive to 2D pose errors. These limitations constrain the full potential of data-driven approaches. To address this, we propose SSR-STF, a dual-stream model that effectively integrates local features with global dependencies to enhance 3D human pose estimation. Specifically, we introduce SSRFormer, a simple yet effective module that employs the skeleton selective refine attention (SSRA) mechanism, leveraging large kernels to capture fine-grained local dependencies in human pose sequences. This complements the global dependencies modeled by the Transformer, enabling a more comprehensive understanding of human motion. By adaptively fusing these two feature streams, SSR-STF can better learn the underlying structure of human poses, overcoming the limitations of traditional methods in local feature extraction. To the best of our knowledge, this is the first work to explore the application of large kernels in skeleton-based 3D human pose estimation. Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that SSR-STF achieves state-of-the-art performance. Furthermore, the motion representations learned by our model prove effective in downstream tasks such as human mesh recovery. Codes are available at SSR-STF.

Non-rigid point cloud registration holds significant importance for human body pose analysis in the fields of sports, medicine, gaming, etc. In this paper, we propose a non-rigid point cloud registration algorithm based on geodesic distance measurement, which can improve the accuracy of the registration for matching point pairs during non-rigid deformations. Firstly, a graph is constructed for two sets of point clouds using geodesic distance measurement considering that geodesic distance changes minimally during non-rigid deformation of the human body, which can preserve the point cloud matching information between corresponding points. Furthermore, a maximal clique search is employed to find combinations of matching pairs between point clouds. Finally, by driving the human body model parameters, sparse matching pairs are overlapped as much as possible to achieve non-rigid point cloud registration of the human body. The accuracy of the proposed algorithm is verified with FAUST and CAPE datasets.