Amazon Technologies, Inc.

Lior Fritz

Matan Goldman

Imry Kissos

Omer Meir

Noam Sorek

Ianir Ideses

Eduard Oks

Dotan Kaufman

Guy Adam

Mark Kliger

Ilia Vitsnudel

Eran Borenstein

Sharon Alpert

Ido Yerushalmy

Eli Alshan

Liza Potikha

Ridge Carpenter

Joel Wilson Brown

Yaar Harari

Georgy Melamed

Lamarr Smith

Elizabeth Reisman

Claire Mcgowan

Characteristics of a user's movement are evaluated based on performance of activities by a user within a field of view of a camera. Video data representing performance of a series of movements by the user is acquired by the camera. Pose data is determined based on the video data, the pose data representing positions of the user's body while performing the movements. The pose data is compared to a set of existing videos that correspond to known errors to identify errors performed by the user. The errors may be used to generate scores for various characteristics of the user's movement. Based on the errors, exercises or other activities to improve the movement of the user may be determined and included in an output presented to the user.

System for correcting user movement

To assist a user in the correct performance of an activity, video data is acquired. A pose of the user is determined from the video data and an avatar is generated representing the user in the pose. The pose of the user is compared to one or more other poses representing correct performance of the activity to determine one or more differences that may represent errors by the user. Depending on the activity that is being performed, some errors may be presented to the user during performance of the activity, while other errors may be presented after performance of the activity has ceased. To present an indication of an error, a specific body part or other portion of the avatar that corresponds to a difference between the user's pose and a correct pose may be presented along with an instruction regarding correct performance of the activity.

Adaptive user interface for determining errors in performance of activities

Devices and techniques are generally described for three dimensional mesh generation. In various examples, first two-dimensional (2D) image data representing a human body may be received from a first image sensor. Second 2D image data representing the human body may be received from a second image sensor. A first pose parameter and a first shape parameter may be determined using a first three-dimensional (3D) mesh prediction model and the first 2D image data. A second pose parameter and a second shape parameter may be determined using a second 3D mesh prediction model and the second 2D image data. In various examples, an updated 3D mesh prediction model may be generated from the first 3D mesh prediction model based at least in part on a first difference between the first pose parameter and the second pose parameter and a second difference between the first shape parameter and the second shape parameter.

Multi-view three-dimensional mesh generation

A first user generates video data for performance of an activity, such as a fitness exercise, by performing the activity in front of a camera. Based on the video data, the amount of movement of different parts of the first user's body is determined. Data representing the position of the first user over time is generated. The data may take the form of a function or a signal that is based on the function. The locations of body parts that move significantly are prioritized over other body parts when determining this data. At a subsequent time, a second user performs the activity. The number of times the second user completes the activity is counted by determining the number of times the second user reaches a position corresponding to a maximum value in the data representing the position of the first user.

System for learning and tracking performance of activities

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Seattle, WA, United States of America

Lior Fritz