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Oren Freifeld

from Cambridge, MA
Age ~46
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Related to:
Limor Freifeld, 42
Connected to:
Brett A Freifeld, 59Jed A Freifeld, 54Julia E Freifeld, 68Julio S Freifeld, 88Roberta I Freifeld, 79Linda Freiha, 38Kathleen M Freihaut, 74Marilyn Freihaut, 64Matthew A Freihaut, 49Robert R Freihaut, 63

Oren Freifeld Phones & Addresses

  • Cambridge, MA
  • Menlo Park, CA
  • Palo Alto, CA
  • Providence, RI
  • 1600 Massachusetts Ave APT 701, Cambridge, MA 02138

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Oren Freifeld Photo 1

Postdoctoral Associate At Mit Eecs

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Location:
Cambridge, Massachusetts
Industry:
Research

Publications

Us Patents

Parameterized Model Of 2D Articulated Human Shape

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US Patent:
20130249908, Sep 26, 2013
Filed:
Jun 8, 2011
Appl. No.:
13/696676
Inventors:
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
International Classification:
G06T 17/20
US Classification:
345420
Abstract:
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.

Parameterized Model Of 2D Articulated Human Shape

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US Patent:
20210398361, Dec 23, 2021
Filed:
Jul 8, 2021
Appl. No.:
17/370272
Inventors:
- 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
International Classification:
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
Abstract:
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

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US Patent:
20190385381, Dec 19, 2019
Filed:
Aug 19, 2019
Appl. No.:
16/544265
Inventors:
- 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
International Classification:
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
Abstract:
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

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US Patent:
20180122146, May 3, 2018
Filed:
Sep 11, 2017
Appl. No.:
15/700686
Inventors:
- 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
International Classification:
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
Abstract:
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

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US Patent:
20170069102, Mar 9, 2017
Filed:
Nov 3, 2016
Appl. No.:
15/342225
Inventors:
- 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
International Classification:
G06T 7/00
G06K 9/00
G06K 9/48
G06T 19/20
G06T 15/20
Abstract:
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

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US Patent:
20160275693, Sep 22, 2016
Filed:
Feb 12, 2016
Appl. No.:
15/042353
Inventors:
- 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
International Classification:
G06T 7/00
G06T 17/20
Abstract:
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.

System And Method For Extracting Dominant Orientations From A Scene

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US Patent:
20150286893, Oct 8, 2015
Filed:
Apr 3, 2015
Appl. No.:
14/678585
Inventors:
- Cambridge MA, US
Guy Rosman - Cambridge MA, US
Oren Freifeld - Cambridge MA, US
John J. Leonard - Newton MA, US
International Classification:
G06K 9/52
G06T 15/10
Abstract:
In one embodiment, a method of identifying the dominant orientations of a scene comprises representing a scene as a plurality of directional vectors. The scene may comprise a three-dimensional representation of a scene, and the plurality of directional vectors may comprise a plurality of surface normals. The method further comprises determining, based on the plurality of directional vectors, a plurality of orientations describing the scene. The determined plurality of orientations explains the directionality of the plurality of directional vectors. In certain embodiments, the plurality of orientations may have independent axes of rotation. The plurality of orientations may be determined by representing the plurality of directional vectors as lying on a mathematical representation of a sphere, and inferring the parameters of a statistical model to adapt the plurality of orientations to explain the positioning of the plurality of directional vectors lying on the mathematical representation of the sphere.
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Oren Freifeld from Cambridge, MA, age ~46 Get Report