Parameterized model of 2D articulated human shape

摘要

A novel “contour person” (CP) model of the human body is proposed that has the expressive power of a detailed 3D model and the computational benefits of a simple 2D part-based model. The CP model is learned from a 3D model of the human body that captures natural shape and pose variations; the projected contours of this model, along with their segmentation into parts forms the training set. The CP model factors deformations of the body into three components: shape variation, viewpoint change and pose variation.;The CP model can be “dressed” with a low-dimensional clothing model, referred to as “dressed contour person” (DCP) model. The clothing is represented as a deformation from the underlying CP representation. This deformation is learned from training examples using principal component analysis to produce so-called eigen-clothing. The coefficients of the eigen-clothing can be used to recognize different categories of clothing on dressed people.;The parameters of the estimated 2D body can be used to discriminatively predict 3D body shape using a learned mapping approach. The prediction framework can be used to estimate/predict the 3D shape of a person from a cluttered video sequence and/or from several snapshots taken with a digital camera or a cell phone.

主权项

1. A method, comprising:
generating a three-dimensional model of an unclothed human body, the three-dimensional model capturing one or more shapes or poses of the unclothed human body; determining two-dimensional contours associated with the three-dimensional model; segmenting the two-dimensional contours into one or more segments; based on the one or more segments, generating a two-dimensional model of the unclothed human body, the two-dimensional model factoring deformations of the unclothed human body into a shape variation component, a viewpoint change, and a pose variation; and generating a second two-dimensional model is of a clothed human body based on the two-dimensional model of the unclothed human body, wherein generating the second two-dimensional model comprises:
computing deformations by aligning a contour of the clothed human body with a second contour of the unclothed human body; andlearning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing.