Hinge Health, Inc.

My Virtual Model Inc.

Wrnch Inc.

Paul Anthony Kruszewski

Colin J Brown

Dongwook Cho

Maggie Zhang

Andrey Tolstikhin

Thomas D Peters

Jean-Francois St-Arnaud

Robert A Lacroix

Wenxin Zhang

Nader Morcos

Sohail Zangenehpour

Louise L Guay

Gregory J Saumier-Finch

Serge Veillet

Andrea M Froncioni

Ryan Russell

Caroline M Trudeau

Fadi G Bachaalani

Sylvain B Cote

Patrick R Guevin

Carlos Saldanha

Jean-Francois B St Arnaud

This disclosure is directed to an activity classifier system, for classifying human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. It also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. There is also an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Method and system for activity classification

An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Introduced here are computer programs that are able to generate computer vision data through local analysis of image data (also referred to as 'raw data' or “input data”). The image data may be representative of one or more digital images that are generated by an image sensor. Also introduced here are apparatuses for generating and handling the image data and computer vision data.

Deriving insights into motion of an object through computer vision

System and method for extracting human pose information from an image, comprising a feature extractor connected to a database, a convolutional neural network (CNN) with a plurality of CNN layers. Said system/method further comprising at least one of the following modules: a 2D body skeleton detector for determining 2D body skeleton information from the human-related image features; a body silhouette detector for determining body silhouette information from the human-related image features; a hand silhouette detector for determining hand silhouette detector from the human-related image features; a hand skeleton detector for determining hand skeleton from the human-related image features; a 3D body skeleton detector for determining 3D body skeleton from the human-related image features; and a facial keypoints detector for determining facial keypoints from the human-related image features.

Human pose analysis system and method

An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of an object. Furthermore, the apparatus includes a memory storage unit to store the raw data. The apparatus also includes a pre-processing engine to generate a coarse segmentation map and a joint heatmap from the raw data. The coarse segmentation map is to outline the object and the joint heatmap is to represent a point on the object. The apparatus further includes a neural network engine to receive the raw data, the coarse segmentation map, and the joint heatmap. The neural network engine is to generate a plurality of two-dimensional maps. Also, the apparatus includes a mesh creator engine to generate a three-dimensional mesh based on the plurality of two-dimensional maps.

Three-dimensional mesh generator based on two-dimensional image

A method and system for providing a computer-simulated environment for displaying a selected mannequin wearing a combination of selected garments. In one aspect, three-dimensional scenes containing mannequin and garment objects are created within a three-dimensional modeling environment, and a simulation is performed using a cloth simulator within the modeling environment to model the construction, draping, and collision of the garment with the mannequin. Rendering frames corresponding to a variety of garments, mannequins, garment dimensions, garment styles, wearing patterns, viewing angles, and other parameters, are then generated from which images can be rendered and displayed in accordance with user requests.

System and method for displaying selected garments on a computer-simulated mannequin

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Westmount, Canada

Paul Anthony Kruszewski