Sol Lee

In this paper, we estimate and refine 3D human pose using the 3D point cloud or mesh model reconstructed from RGB-D cameras or volumetric capture systems. We first reconstruct the 3D model using the multi-view cameras to estimate a highly accurate skeleton. To obtain a 2D skeleton with low error, the reconstructed 3D model is projected to four virtual planes after decidi ng the direction of the 3D model. Four 2D skeletons are estimated from four images projected in the virtual plane. Afterward, the refinement process selects candidate joints based on the distribution of local vertices and the DBSCAN algorithm. It applies a sphere fitting to ensure that the final joints are located within the body volume. The joints are combined at the intersection through the back-projection of the joints, including those in the 2D skeleton on the virtual plane. The joints in the intersection are refined using the spatial distribution of the 3D information. Through the proposed method, we estimated a stable and geometrically consistent 3D human pose from reconstructed volumetric data. Using models with ground truth, we calculated the MPJPE between the skeletons of the proposed and the ground truth. The 3D pose estimation was evaluated through a visual assessment of the captured image, and the results were quantitatively compared with the 3D joint positions acquired by the motion capture device.

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud using temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCF), and a key PCF is selected. After the 3D skeleton is predicted through 3D pose estimation, the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCF by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method’s effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%.

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user’s device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user’s mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.