When athletes perform different sports, the angles of joints and the speed of movement are different, which causes the feature capture points to not correspond to the key areas well. This results in forming feature messy correspondence errors, and affecting the accuracy of shape basis calculation. The traditional 3D reconstruction algorithms for sports images are affected by the messy profile, and it is difficult to counter the accuracy of later modeling. Therefore, an error control mechanism in the virtual modernization of random sports image collected points is recommended for the complex systems. In this work, the factorization method is used to establish a 3-D dynamic simulation structure of the human body when it is moving. The stolt transformation is implemented for adjusting the azimuth offset rate of all image capturing regions, such that it cannot generate excessive errors during feature matching. The large messy residual error is used for third-order signal compensation for realizing the 3-D dynamic simulation structure in the humanoid signal image sequence. The observed outcomes shows that this approach improves the authenticity of 3-D dynamic simulation of human signal image classifications. Using 150 images and 915 key points, an authenticity coefficient of 0.86 is achieved by the proposed approach outperforming the other state-of-the-art algorithms.

当运动员进行不同的运动时，关节的角度和运动的速度都不同，这将导致特征捕获点与关键区域的对应性不佳。这导致形成特征混乱的对应误差，并影响形状基准计算的准确性。运动图像的传统3D重建算法会受到凌乱轮廓的影响，因此很难抵消以后建模的准确性。因此，对于复杂的系统，建议对随机运动图像采集点进行虚拟现代化时的错误控制机制。在这项工作中，分解方法用于建立人体移动时的3-D动态仿真结构。执行stosto转换以调整所有图像捕获区域的方位角偏移率，这样它就不会在特征匹配过程中产生过多的错误。大的残差残差用于三阶信号补偿，以实现人形信号图像序列中的3-D动态仿真结构。观察到的结果表明，这种方法提高了人类信号图像分类的3D动态仿真的真实性。通过使用150张图像和915个关键点，所提方法的真实性系数达到0.86，优于其他最新算法。观察到的结果表明，这种方法提高了人类信号图像分类的3D动态仿真的真实性。通过使用150张图像和915个关键点，所提方法的真实性系数达到0.86，优于其他最新算法。观察到的结果表明，这种方法提高了人类信号图像分类的3D动态仿真的真实性。通过使用150张图像和915个关键点，所提方法的真实性系数达到0.86，优于其他最新算法。