Brown University

Google Inc.

Peng Guan

Michael Julian Black

Matthew M Loper

Alexander W Weiss

Oren Freifeld

Chaitrali Amrutkar

Michael J Black

Methods, systems, and media for performing personalized actions on mobile devices associated with a media presentation device are provided. In some implementations, the method comprises: determining that a mobile device is associated with a media presentation device over a communications network, wherein the media presentation device is in an operating mode that presents a sequence of images; receiving a user command that indicates interest in an image from the sequence of images being presented on the media presentation device; determining metadata associated with the image from the sequence of images being presented on the media presentation device; causing the metadata associated with the image to be stored with a representation of the image from the sequence of images and a user account identifier corresponding to a user of the mobile device; receiving a request to review saved image information corresponding to one or more images in which the user command indicative of interest has been received; and in response to receiving the request, causing a user interface element to be presented that allows a user of the mobile device to navigate through the saved image information, wherein the user interface element includes at least the representation of the image from the sequence of images and information relating to the image from the sequence of images.

Methods, systems, and media for storing information associated with content presented on a media presentation device

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

A system generates a clothing deformation model which models one or more of a pose-dependent clothing shape variation which is induced by underlying body pose parameters, a pose-independent clothing shape variation which is induced by clothing size and underlying body shape parameters and a clothing shape variation including a combination of the pose-dependent clothing shape variation and/or the pose-independent clothing shape variation. The system generates, for an input human body, a custom-shaped garment associated with a clothing type by mapping, via the clothing deformation model, body shape parameters of the input human body to clothing shape parameters of the clothing type and dresses the input human body with the custom-shaped garment.

System and method for simulating realistic clothing

Methods, systems, and media for performing personalized actions on mobile devices associated with a media presentation device are provided. In some implementations, the method comprises: determining that a mobile device is associated with a media presentation device, wherein the media presentation device is in an operating mode that presents a sequence of images; receiving a user command that indicates interest in an image from the sequence of images; determining metadata associated with the image; causing the metadata to be stored with a representation of the image and a user account identifier; receiving a request to review saved image information corresponding to one or more images in which the user command indicative of interest has been received; and, causing a user interface element to be presented that allows a user of the mobile device to navigate through the saved image information.

Systems, methods, and computer-readable storage media for simulating realistic clothing. The system generates a clothing deformation model for a clothing type, wherein the clothing deformation model factors a change of clothing shape due to rigid limb rotation, pose-independent body shape, and pose-dependent deformations. Next, the system generates a custom-shaped garment for a given body by mapping, via the clothing deformation model, body shape parameters to clothing shape parameters. The system then automatically dresses the given body with the custom-shaped garment.

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.

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Mountain View, CA, United States of America

Peng Guan