Max-planck-gesellschaft Zur Forderung Der Wissenschaften E.v.

Eric Rachlin

Michael Julian Black

Matthew Maverick Loper

Nicolas Heron

Alexander Weiss

Matthew M Loper

David L Hirshberg

Alex Weiss

David Allen Smith

Steven Douglas Hatch

Javier Romero Gonzalez-Nicolas

Xiaochen Hu

Theodora Hinkle

Martin Kristiansen

Evan Lee

Jonathan Robert Cilley

William John O'Farrell

Jason Lawrence Gelman

Disclosed are systems and techniques for extracting user body shape (e.g., a representation of the three-dimensional body surface) from user behavioral data. The behavioral data may not be explicitly body-shape-related, and can include shopping history, social media likes, or other recorded behaviors of the user within (or outside of) a networked content delivery environment. The determined body shape can be used, for example, to generate a virtual fitting room user interface.

Machine learning systems and methods for extracting user body shape from behavioral data

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

Disclosed is a method including receiving an input image including a human, predicting, based on a convolutional neural network that is trained using examples consisting of pairs of sensor data, a corresponding body shape of the human and utilizing the corresponding body shape predicted from the convolutional neural network as input to another convolutional neural network to predict additional body shape metrics.

Machine learning systems and methods of estimating body shape from images

Disclosed is a method including receiving visual input comprising a human within a scene, detecting a pose associated with the human using a trained machine learning model that detects human poses to yield a first output, estimating a shape (and optionally a motion) associated with the human using a trained machine learning model associated that detects shape (and optionally motion) to yield a second output, recognizing the scene associated with the visual input using a trained convolutional neural network which determines information about the human and other objects in the scene to yield a third output, and augmenting reality within the scene by leveraging one or more of the first output, the second output, and the third output to place 2D and/or 3D graphics in the scene.

Machine learning systems and methods for augmenting images

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).

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Brooklyn, NY, United States of America

Eric Rachlin