Motorola Solutions, Inc.

Amazon Technologies, Inc.

Motorola Corporation

Apoorv Chaudhri

Yadunandana N Rao

Prakash Ramu

Amit Kumar Agrawal

Rohith Mysore Vijaya Kumar

David P Gurney

Ambrish Tyagi

Visesh Uday Kumar Chari

Ram Sever

Sunil Sharadchandra Hadap

Matthew E Simms

Matthew Alan Townsend

Eduard Oks

Yadunandana Nagaraja Rao

Brandon Michael Smith

Jinjin Li

Robert J Corke

Scott Michael Alazraki

Darrell James Stogner

Alan P Conrad

Stanko Jelavic

Rajesh Gautam

Stephen Leigh Kuffner

Christopher G Ware

Daniel G Prysby

Siddhartha Chandra

Michael F Zeleznik

Geetha K Thampi

Trefor J Delve

Surhir Rao

Dylan John Drover

Antonio Criminisi

Charles R Ruelke

Ellis Arthur Pinder

Noam Sorek

Jesus Francisco Corretjer

Robert D LoGalbo

Leslie G Gustafson

Joseph Olk Lester

Oleg Andric

Chet A Lampert

Nicolas Heron

Abhishek Singh

Bob D Logalbo

Ralf Hekmann

Oleksandr Vorobiov

Roberto A Perez

Ita Lifshitz

Enrica Maria Filippi

Anantharanga Prithviraj

Sudhir Rao

Stan Jelavic

F Noam Sorek

Gal Levi

Anna Shtengel

Ayush Sharma

Sigal Raab

Cyrus Fazeli

Wei-Lin Hsiao

Stephen L Kuffner

Techniques are described herein for generating a virtual try-on augmented image. An example method can include A system can access a first two-dimensional model of a target garment. The system can determine a first plurality of shape keypoints for a first part of the first plurality of parts of the image. The system can access a second two-dimensional image of a first user. The system can determine a second plurality of keypoints for a second part of the second plurality of parts of the second two-dimensional image, a second number of the second plurality of keypoints based at least in part on a second predetermined number of keypoints associated with the second part. The system can generate a rendered augmented image of the target garment on the target user.

Model-based augmented image generation

Described are systems and method directed to generation of a dimensionally accurate three-dimensional ('3D') model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different views with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model or avatar of the body of the user.

Three-dimensional body model from a two-dimensional body image

Described are systems and methods directed to a body change journey that utilizes collected two-dimensional ('2D') body images of a body of a user, generation and presentation of a personalized three-dimensional (“3D”) body model of the body of the user based on those 2D body images, the generation and presentation of different predicted personalized 3D body models of the body of the user at different body measurements (e.g., different body fat percentages, different muscle mass amounts, different body weights, etc.), and the generation of a body change journey that guides the user from their current body model to a selected target body model that is represented by a target personalized 3D body model of the body of the user.

Personalized three-dimensional body models and body change journey

Described are systems and methods that use one or more two-dimensional ('2D') body images of a body to determine body fat measurements of that body. For example, a standard 2D camera of a portable device, such as a cell phone, tablet, laptop, etc., may be used to generate one or more 2D body images of a user. Those 2D body images, or image, may be processed using the disclosed implementations to determine a body fat measurement of the body represented in the image.

Body fat measurements from a two-dimensional image

Described are systems and methods directed to generation and subsequent update of a dimensionally accurate body model of a body, such as a human body, based on two-dimensional ('2D') images of at least a portion of that body and/or face images of a face of the body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) to produce body images that are used to generate a body model of the body of the user. Subsequently, the body model may be updated based on a face image of the face of the user, without requiring the user to provide another body image.

Body model composition update from two-dimensional face images

Described are systems and methods directed to generation of a personalized three-dimensional ('3D') body model of a body, such as a human body, based on two-dimensional ('2D') images of that body and the generation and presentation of predicted personalized 3D body models of the body when one or more body measurements (e.g., body fat, body weight, muscle mass) are changed. For example, a user may provide a target body measurement value and the implementations will generate one or more predicted personalized 3D body models representative of a predicted appearance of the body with the target body measurement value.

Generation and presentation of predicted personalized three-dimensional body models

Devices, systems and methods are disclosed for improving story assembly and video summarization. For example, video clips may be received and a theme may be determined from the received video clips based on annotation data or other characteristics of the received video data. Individual moments may be extracted from the video clips, based on the selected theme and the annotation data. The moments may be ranked based on a priority metric corresponding to content determined to be desirable for purposes of video summarization. Select moments may be chosen based on the priority metric and a structure may be determined based on the selected theme. Finally, a video summarization may be generated using the selected theme and the structure, the video summarization including the select moments.

Video ingestion and clip creation

Described are systems and method directed to generation of a dimensionally accurate three-dimensional ('3D') model of a body, such as a human body, based on two-dimensional ('2D') images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different views with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model or avatar of the body of the user.

Three-dimensional body composition from two-dimensional images

Predictive personalized three-dimensional body models

Devices, systems and methods are disclosed for reducing a perceived latency associated with uploading and annotating video data. For example, video data may be divided into video sections that are uploaded individually so that the video sections may be annotated as they are received. This reduces a latency associated with the annotation process, as a portion of the video data is annotated before an entirety of the video data is uploaded. In addition, the annotation data may be used to generate a master clip table and extract individual video clips from the video data.

Devices and techniques are generally described for image editing. In various examples, color image data and segmentation image data may be identified. In some examples, the segmentation image data may identify a first portion of the color image data as a foreground region and a second portion of the color image data as a background region. A segmentation boundary may be identified between the foreground region and the background region. A first area of the second portion may be identified, where the first area extends from the segmentation boundary to a first area outer boundary. A second area of the second portion may be identified, where the second area extends from the first area outer boundary to a second area outer boundary. A blended and blurred representation of the first area and the second area may be generated.

Generation of semantic depth of field effect

A computing platform supports stream processing pipelines, each of which comprises a sequence of stream processing tools. Upon specification of a stream processing pipeline, multiple available hardware processors are evaluated to determine which of the processor is capable of executing each tool of the pipeline while satisfying specified performance goals. Among these processors, a hardware processor is selected for each pipeline tool that will minimize power consumption.

Dynamic selection of hardware processors for stream processing

A technique for a secondary communication system to utilize spectrum designated to another (or primary) communication system is provided. By ranking a plurality of secondary base stations based on base station transmit power, calculated required transmit power and path loss, a set of criteria is developed for selecting a highest ranked secondary base station for operation within a primary's spectrum. The ranking may be adapted based on mobility of the secondary's subscriber; and as such the secondary system communicates within the primary's spectrum using the adaptively ranked base stations. Channel selection may also be ranked. The technique and apparatus allows a cognitive radio (CR) network to operate within an incumbent network's spectrum.

Method and apparatus for optimizing spectrum utilization by a cognitive radio network

Growing community of inventors

Sunrise, FL, United States of America

Apoorv Chaudhri