Semi-supervised Learning of Joint Density Models for Human Pose Estimation

Learning regression models (for example for body pose estimation, or BPE) currently requires large numbers of training examples—pairs of the form (image, pose parameters). These examples are difficult to obtain for many problems, demanding considerable effort in manual labelling. However it is easy to obtain unlabelled examples—in BPE, simply by collecting many images, and by sampling many poses using motion capture. We show how the use of unlabelled examples can improve the performance of such estimators, making better use of the difficult-to-obtain training examples. Because the distribution of parameters conditioned on a given image is often multimodal, conventional regression models must be extended to allow for multiple modes. Such extensions have to date had a pre-set number of modes, independent of the contents of the input image, and amount to fitting several regressors simultaneously. Our framework models instead the joint distribution of images and poses, so the conditional estimates are inherently multimodal, and the number of modes is a function of the joint-space complexity, rather than of the maximum number of output modes. We demonstrate the improvements obtainable by using unlabelled samples on synthetic examples and on a real pose estimation problem, and demonstrate in both cases the additional accuracy provided by the use of unlabelled data.