2011 18th IEEE International Conference on Image Processing INFERRING 3D BODY POSE USING VARIATIONAL SEMI-PARAMETRIC REGRESSION

To deal with multi-modality in human pose estimation, mixture models or local models are introduced. However, problems with over-fitting and generalization are caused by our necessarily limited data, and the regression parameters need to be determined without resorting to slow and processorhungry techniques, such as cross validation. To compensate these problems, we have developed a semi-parametric regression model in latent space with variational inference. Our method performed competitively in comparison to other current methods. Index Terms — Image motion analysis, unsupervised learning, regression model, latent variable mode