Amazon Technologies, Inc.

Gitika Karumuri

Sunil Sharadchandra Hadap

Ashwin Swaminathan

Yuelong Li

Miriam Bellver Bueno

Xin Shen

Amogh Gupta

A first region within image data corresponding to a first portion of a user is determined, and a classifier associated with the first portion of the user is determined. A second region within the image data that includes the first region is determined. Within the second region, first pixels corresponding to the first portion of the user, second pixels corresponding to a second portion of the user adjoined to the first portion of the user, and third pixels corresponding to an environment of the user are identified. Based on identifying the second pixels, the classifier associated with the first portion of the user is confirmed. Upon the confirmation an item is rendered on the first portion of the user.

Reducing false positive detections based on classification and segmentation cues

The present disclosure describes a system and method for implementing a virtual shoe try-on feature. The method includes predicting, using a machine learning model, a first plurality of pixels including pixels of an image corresponding to a foot in the image and adding a first virtual cylindrical object to a three-dimensional model of a shoe such that a portion of the first virtual cylindrical object is positioned within the three-dimensional model of the shoe. The method also include determining a set of pixels including pixels of the first plurality of pixels that intersect with the first virtual cylindrical object and removing, from the three-dimensional model of the shoe, a first portion of the three-dimensional model of the shoe that intersects with the set of pixels.

Virtual shoe try-on

The present disclosure describes a system and method for implementing a virtual shoe try-on feature. The method includes receiving, from a database, a three-dimensional model of a shoe with a first annotated point on the three-dimensional model of the shoe, a second annotated point on the three-dimensional model of the shoe, and a third annotated point on the three-dimensional model of the shoe. The method also includes predicting, using a machine learning model and based on an image of a foot, one or more features of the foot, generating, based on the first annotated point, the second annotated point, the third annotated point, and the one or more features, a two-dimensional model of the shoe using the three-dimensional model of the shoe, and superimposing the two-dimensional model of the shoe onto the image of the foot.

A method includes extracting, using a backbone of a machine learning model, a plurality of features from an image of a foot and predicting, using a first portion of the machine learning model and based on one or more features of the plurality of features, a first aspect of the foot. The method also includes predicting, using a second portion of the machine learning model and based on one or more features of the plurality of features, a second aspect of the foot different from the first aspect, generating, using at least the first aspect and the second aspect, a two-dimensional model of a shoe, and superimposing the two-dimensional model of the shoe onto the image of the foot.

Growing community of inventors

Santa Clara, CA, United States of America

Gitika Karumuri