Alexander W Weiss

20100111370, May 6, 2010

Aug 14, 2009

12/541898

Michael J. Black - Providence RI, US
Alexandru O. Balan - Pawtucket RI, US
Alexander W. Weiss - Shirley MA, US
Leonid Sigal - Pittsburgh PA, US
Matthew M. Loper - Providence RI, US
Timothy S. St. Clair - Cambridge MA, US

G06K 9/00
G06Q 30/00
G06Q 50/00
H04N 7/18
G06K 9/34
G06K 9/68

382111, 705 27, 348 77, 382173, 382164, 382218, 348E07085

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.

20130249908, Sep 26, 2013

Jun 8, 2011

13/696676

Michael J. Black - Tuebingen, DE
Oren Freifeld - Menlo Park CA, US
Alexander W. Weiss - Shirley MA, US
Matthew M. Loper - Tuebingen, DE
Peng Guan - Mountain View CA, US

G06T 17/20

345420

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.

20210398361, Dec 23, 2021

Jul 8, 2021

17/370272

- Providence RI, US
Oren Freifeld - Menlo Park CA, US
Alexander W. Weiss - Shirley MA, US
Matthew M. Loper - Tuebingen, DE
Peng Guan - Mountain View CA, US

G06T 19/20
G06K 9/00
G06K 9/48
G06T 7/13
G06T 7/149
G06K 9/62
G06T 17/00
G06T 17/20
G06T 15/20

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.

20190385381, Dec 19, 2019

Aug 19, 2019

16/544265

- Providence RI, US
Oren Freifeld - Menlo Park CA, US
Alexander W. Weiss - Shirley MA, US
Matthew M. Loper - Tuebingen, DE
Peng Guan - Mountain View CA, US

G06T 19/20
G06K 9/00
G06T 17/20
G06T 17/00
G06T 15/20
G06T 7/149
G06T 7/13
G06K 9/62
G06K 9/48

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.

20190333267, Oct 31, 2019

Jun 26, 2019

16/453135

- Providence RI, US
Alexandru O. BALAN - Pawtucket RI, US
Alexander W. Weiss - Shirley MA, US
Leonid SIGAL - Pittsburgh PA, US
Matthew M. LOPER - Providence RI, US
Timothy S. ST. CLAIR - Cambridge MA, US

G06T 17/00
G06K 9/62
G06T 7/73
G06T 7/77
G06K 9/00
G06Q 30/06

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.

20180293788, Oct 11, 2018

Jun 14, 2018

16/008637

- Providence RI, US
Alexandru O. BALAN - Pawtucket RI, US
Alexander W. Weiss - Shirley MA, US
Leonid SIGAL - Pittsburgh PA, US
Matthew M. LOPER - Providence RI, US
Timothy S. ST. CLAIR - Cambridge MA, US

G06T 17/00
G06T 7/77
G06T 7/73
G06Q 30/06
G06K 9/62
G06K 9/00

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.

20180122146, May 3, 2018

Sep 11, 2017

15/700686

- Providence RI, US
Oren Freifeld - Menlo Park CA, US
Alexander W. Weiss - Shirley MA, US
Matthew M. Loper - Tuebingen, DE
Peng Guan - Mountain View CA, US

G06T 19/20
G06K 9/00
G06K 9/48
G06K 9/62
G06T 7/13
G06T 7/149
G06T 15/20
G06T 17/00
G06T 17/20

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.

20170069102, Mar 9, 2017

Nov 3, 2016

15/342225

- Providence RI, US
Oren Freifeld - Menlo Park CA, US
Alexander W. Weiss - Shirley MA, US
Matthew M. Loper - Tuebingen, DE
Peng Guan - Mountain View CA, US

G06T 7/00
G06K 9/00
G06K 9/48
G06T 19/20
G06T 15/20

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.