基于Kinect的人体姿态估计优化和动画生成

为了生成更准确流畅的虚拟人动画，采用Kinect设备捕获三维人体姿态数据的同时，使用单目人体三维姿态估计算法对Kinect的彩色信息进行骨骼点数据推理，从而实时优化人体姿态估计效果，并驱动虚拟人物模型生成动画。首先，提出了一种时空优化的骨骼点数据处理方法，以提高单目估计人体三维姿态的稳定性；其次，提出了一种Kinect和遮挡鲁棒姿势图（ORPM）算法融合的人体姿态估计方法来解决Kinect的遮挡问题；最后，研制了基于四元数向量插值和逆向运动学约束的虚拟人动画系统，其能够进行运动仿真和实时动画生成。与仅利用Kinect捕获人体运动来生成动画的方法相比，所提方法的人体姿态估计数据鲁棒性更强，具备一定的防遮挡能力，而与基于ORPM算法的动画生成方法相比，所提方法生成的动画在帧率上提高了两倍，效果更真实流畅。

Kinect-based human pose estimation optimization and animation generation

In order to generate more accurate and smooth virtual human animation， the Kinect device was used to capture the 3D human body pose data， and the monocular 3D human body pose estimation algorithm was used to reason the skeleton points in the color information of the Kinect at the same time， thereby optimizing the human pose estimation effect at real time， and driving the virtual character model to generate animation. Firstly， a spatio-temporal optimization method of skeleton point data processing was proposed to improve the stability of monocular estimation of the 3D human body pose. Secondly， a human pose estimation method based on the fusion of Kinect and Occlusion-Robust Pose-Maps （ORPM） algorithm was proposed to solve the occlusion problem of Kinect. Finally， a virtual human animation system based on quaternion vector interpolation and inverse kinematics constraints was developed， which was able to perform motion simulation and real-time animation generation. Compared with the animation generation method that only uses Kinect to capture human motion， the proposed method has the human body estimation data more robust， and has a certain anti-occlusion ability. The animation frame rate generated by this method is two times higher compared to that of the ORPM-based animation generation method， so that the effect of the animation generated by the proposed method is more realistic and smooth.