Optimization algorithm for estimating the human pose by using the morphable model

An optimization algorithm is proposed utilizing the video data and point cloud data captured by the depth camera to solve the problems such as error-proneness and incoherence of motion sequence caused by the existing human pose estimation algorithms based on the morphable model. For video data, the neural network is first used in extracting the model parameters from each color image frame. Next, the human key-points and contour constraint are considered to optimize the above parameters. Then the coherence between every two consecutive frames is utilized to correct the error of pose estimation, thus making the resulting motion sequence smoother. In addition, the point cloud and the model obtained from the corresponding color image frame are used as the joint input to further improve the estimation accuracy. Finally, the distance between the point cloud and the corresponding point of the model is constrained to be as small as possible to obtain a more reasonable solution. The proposed algorithm and the state-of-the-art algorithms are compared qualitatively and quantitatively on the data set and real video set. Experimental results show that the algorithm can effectively correct the error and incoherence in the single-frame pose estimation results and greatly improve the accuracy when using point cloud data optimization.