Brown University

Max-planck-gesellschaft Zur F&#xf6;rderung Der Wissenschaften E. V.

Irobot Corporation

Matthew M Loper

Michael Julian Black

Alexander W Weiss

Peng Guan

Oren Freifeld

Leonid Sigal

Alexandru O Balan

Timothy S St Clair

Eric Rachlin

Naureen Mahmood

David L Hirshberg

Alex Weiss

Christopher Vernon Jones

Odest Chadwicke Jenkins

Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

Parameterized model of 2D articulated human shape

Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co-registering of the measured object scan data (S).

Co-registration&#x2014;simultaneous alignment and modeling of articulated 3D shapes

A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appearin minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Method and apparatus for estimating body shape

A method for providing a three-dimensional body model which may be applied for an animation, based on a moving body, wherein the method comprises providing a parametric three-dimensional body model, which allows shape and pose variations; applying a standard set of body markers; optimizing the set of body markers by generating an additional set of body markers and applying the same for providing 3D coordinate marker signals for capturing shape and pose of the body and dynamics of soft tissue; and automatically providing an animation by processing the 3D coordinate marker signals in order to provide a personalized three-dimensional body model, based on estimated shape and an estimated pose of the body by means of predicted marker locations.

Method for providing a three dimensional body model

A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co registering of the measured object scan data (S).

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 &#x201c;dressed contour person&#x201d; (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.

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 20 part-based model. The CP model is learned from a 3D model of the human body that captures natural shape and pose variations. 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. 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 20 body can be used to discriminatively predict 3D body shape using a learned mapping approach.

A method for facilitating cooperation between humans and remote vehicles comprises creating image data, detecting humans within the image data, extracting gesture information from the image data, mapping the gesture information to a remote vehicle behavior, and activating the remote vehicle behavior. Alternatively, voice commands can by used to activate the remote vehicle behavior.

Growing community of inventors

Tuebingen, Germany

Matthew M Loper