3D human pose estimation from depth maps using a deep combination of poses

Abstract

Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.

Reconstructing Three-Dimensional Human Poses: A Combined Approach of Iterative Calculation on Skeleton Model and Conformal Geometric Algebra

“…That is, the proposed method satisfactorily locates joint points. In addition, the located errors of joint points g 8 and g 14 have the lowest value in the 30th and 40th frames of the motion sequence images (See Figure 10). The reason is that the points of the left wrist and right ankle are nearer to the camera in the above frames.…”

“…To estimate the accuracy of the tracking of human motion poses, the joint points obtained by the proposed automatic location method are compared to the accurate joint points based on the manual location method (see Figure 11). The width error of the located right wrist point 7 g , left wrist point 8 g , right ankle point 14 g and left ankle point 15 g , based on the human motion sequence images shown in Figure 10, are shown in Figure 11a. The image shown in Figure 11b corresponds to the located height error of the above joint points.…”

“…The reason is attributed to the phenomenon of the distances among all of the joint points decreasing when sitting people are considered, so that several joint points are often mistakenly recognized. In addition, consider the poses 13,14,15,16,17,19,20,22,23,24,27,28,29, which correspond to people with joint point occlusion. The proposed method was superior to [35] by 1.46% PCKh (91.05% vs. 89.59%), [34] by 0.44% PCKh (91.05% vs. 90.61%), [33] by 0.15% PCKh (91.05% vs. 90.90%) and [31] by 0.11% PCKh (91.05% vs. 90.94%), considering the average of the above poses.…”

“…In recent years, deep learning has made considerable progress and also obtained satisfactory results in human pose estimation. Marin-Jimenez et al [14] proposed a deep depth pose model to obtain 3D positions of body joints and reconstruct human poses. Hong et al [15] improved traditional methods by adopting locality preserved restriction, based on a denoising auto-encoder for estimating 3D human poses.…”

3D Joints Estimation of the Human Body in Single-Frame Point Cloud

“…Li et al [25] addressed the problem of multiscale graph, using dynamic neural networks to predict 3D skeleton-based human motions. Mariń-Jiménez et al [26] proposed a Deep Depth Pose (DDP) model, where 3D human pose in a depth image was designed by linear combination of some predefined pose prototypes, and the human body 3D joints were determined. Zhang et al [27] used PointNet++ network to estimate human joint points, but it required 2D joints as prior information, and could not directly obtain joints on the point cloud.…”