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MNN-IK: Mixing neural network and numerical IK for human posing

Summary: The inverse kinematics of human ﬁgure is the basic problem of human motion synthesis, human motion capture and understanding. The system of kinematic constraint equations has many or no solutions to the inverse kinematics for human posing because the human system is very complex and its articulated representation has many degrees of freedom. Traditional methods usually use analytical or numerical iterative methods to solve the inverse kinematics problem. They can obtain good results with suﬃcient constraints. However, it is diﬃcult to ﬁnd natural human pose only given a small number of constraints. In recent years, the idea of learning statistical models from large-scale data sets has been widely used. Among the machine learning methods for solving inverse kinematics of human body, the classical work GMM-IK proposes to use Gaussian mixture model to construct the human posture data distribution, and uses the expectation maximization method to solve the parameters. This paper presents a method combining neural networks and numerical inverse kinematics for human posing. It can synthesize natural human pose with a small number of constraints. Extensive quantitative experiments show that the joint coordinates and angle reconstruction errors of our method are reduced by an average of 25% and 39%, respectively, compared with the state of the art, i.e., Gaussian mixture model. Our method can be used to deal with optical motion capture data, and estimate human pose in RGB image or video.