Latent Gaussian Mixture Regression for Human Pose Estimation

Discriminative approaches for human pose estimation model the functional mapping, or conditional distribution, between image features and 3D pose. Learning such multi-modal models in high dimensional spaces, however, is challenging with limited training data; often resulting in over-fitting and poor generalization. To address these issues latent variable models (LVMs) have been introduced. Shared LVMs attempt to learn a coherent, typically non-linear, latent space shared by image features and 3D poses, distribution of data in that latent space, and conditional distributions to and from this latent space to carry out inference. Discovering the shared manifold structure can, in itself, however, be challenging. In addition, shared LVMs models are most often non-parametric, requiring the model representation to be a function of the training set size. We present a parametric framework that addresses these shortcoming. In particular, we learn latent spaces, and distributions within them, for...

• Type: Journal
• Year: 2010
• Where: ACCV
• Authors: Yan Tian, Leonid Sigal, Hernán Badino, Fernando De la Torre, Yong Liu
• Keywords: ACCV 2010, Computer Vision, Conditional Distributions, Image Features, Latent Space