Amazon Technologies, Inc.

Mark Kliger

Noam Sorek

Ianir Ideses

Eduard Oks

Ido Yerushalmy

Dotan Kaufman

Eli Alshan

Liza Potikha

Sharon Alpert

Omer Meir

Lior Fritz

Matan Goldman

Imry Kissos

Guy Adam

Eran Borenstein

Ridge Carpenter

Georgy Melamed

Lamarr Smith

Elizabeth Reisman

Claire Mcgowan

Yaar Harari

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

Video output is generated based on first video data that depicts the user performing an activity. Poses of the user during performance of the activity are compared with second video data that depicts an instructor performing the activity. Corresponding poses of the user's body and the instructor's body may be determined through comparison of the first and second video data. The video data is used to determine the rate of motion of the user and to generate video output in which a visual representation of the instructor moves at a rate similar to the that of the user. For example, video output generated based on an instructional fitness video may be synchronized so that movement of the presented instructor matches the rate of movement of the user performing an exercise, improving user comprehension and performance.

System for synchronizing video output based on detected behavior

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

Systems for assisting a user in performance of a meditation activity or another type of activity are described. The systems receive user input and sensor data indicating physiological values associated with the user. These values are used to determine a recommended type of activity and a length of time for the activity. While the user performs the activity, sensors are used to measure physiological values, and an output that is provided to the user is selected based on the measured physiological values. The output may be selected to assist the user in reaching target physiological values, such as a slower respiration rate. After completion of the activity, additional physiological values are used to determine the effectiveness of the activity and the output that was provided. The effectiveness of the activity and the output may be used to determine future recommendations and future output.

Interface for guided meditation based on user interactions

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Modi'in, Israel

Mark Kliger