Amazon Technologies, Inc.

Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Cisco Technology, Inc.

Amit Kumar Agrawal

Ambrish Tyagi

Ramesh Raskar

Rohith Mysore Vijaya Kumar

Prakash Ramu

Ashok Veeraraghavan

Visesh Uday Kumar Chari

Apoorv Chaudhri

Yadunandana Nagaraja Rao

Brandon Michael Smith

Jinjin Li

Yuichi Taguchi

Qiang Liu

Siddhartha Chandra

Suresh Bholabhai Lakhani

Ram Sever

Sunil Sharadchandra Hadap

Jay E Thornton

Sharadh Ramaswamy

Srikumar Ramalingam

Abhishek Singh

David Ting-Yu Wu

Shashank Tripathi

Oncel C Tuzel

Stephen Vincent Mangiat

Kah Kuen Fu

Jim Oommen Thomas

Cheng-Hao Kuo

James Rehg

Lelin Zhang

Tianyang Ma

Sisil Sanjeev Mehta

Rajesh Gautam

Shree Kumar Nayar

Toru Sasaki

Hajime Nakajima

Ilya Vladimirovich Brailovskiy

Osamu Nasu

Takeshi Musha

Yoshinao Tatei

Aswin C Sankaranarayanan

Takuya Noguchi

Zachary Lawrence Brand

Gerald A Welch

Durga Venkata Kiran Yakkala

Shigenori Takeda

Yuanzhen Li

Visesh Chari

Jack Tumblin

Dylan John Drover

Haruhiko Takeyama

Dikpal N Reddy

Matthew E Brand

Antonio Criminisi

Timothy Thomas Gray

Noam Sorek

Mohit Gupta

Wei Li

Madhav V Marathe

Siddharth Choudhary

Huseyin Erdim

Madhu Grandhi

Nicolas Heron

Makito Seki

Weijian Wang

Larry Davis

Alexander Adrian Hugh Davidson

Ita Lifshitz

Anantharanga Prithviraj

Oleksandr Vorobiov

Ju Yong Chang

Viveka Kulharia

Siddhant Ranade

Ganesh Subramanian Iyer

Gal Levi

Anna Shtengel

Ayush Sharma

Sigal Raab

Cyrus Fazeli

Osama Makansi

Nina Mitra Khosrowsalafi

Nicholas Jialei Su

Changhua Wu

Jay E Thorton

Yi Xu

Cyrus A Wilson

Systems and methods are described for generating images of synthesized bodies wearing a garment. For instance, a source image of a human or mannequin wearing a garment may be submitted to a synthesized human generation system. In response to receiving the source image, the synthesized human generation system may use a classifier to classify the image as depicting one or more body types or orientations. The synthesized human generation system may also apply segmentation to the source image to segment the garment pixels. The synthesized human generation system may then select one or more body generation machine learning models based on the classification of the source image. The synthesized human generation system may utilize the selected machine learning models to generate one or more output images of synthesized bodies that appear to be wearing the garment, using the segmented garment as input.

Generating images of synthesized bodies wearing a garment

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

Techniques for disabling a sensor are described. In an example, the techniques include receiving input data indicating a request to disable a sensor of the device. Based on the input data, the sensor is disabled by at least decoupling the sensor from a power source and decoupling the sensor from an input terminal of a processor of the device. The techniques also include sending, to a plurality of clients that are implemented on the device and that are configured to receive sensor data generated by the sensor, notification data indicating that the sensor is disabled. Further, the techniques include outputting, by a user interface of the device, an indication that the sensor is disabled.

Controlling sensors of a device and notifying sensor clients

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 to determine one or more body dimensions of a body based on a processing of one or more two-dimensional images that include a representation of the body. Body dimensions include any length, circumference, etc., of any part of a body, such as shoulder circumference, chest circumference, waist circumference, hip circumference, inseam length, bicep circumference, leg circumference, etc.

Body dimensions from two-dimensional body images

Described are systems and methods 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 at least a portion 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 at least a portion 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 of a portion of a body

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

Techniques are generally described for object detection in image data. First image data comprising a three-dimensional model representing an object may be received. First background image data comprising a first plurality of pixel values may be received. A first feature vector representing the three-dimensional model may be generated. A second feature vector representing the first plurality of pixel values of the first background image data may be generated. A first machine learning model may generate a transformed representation of the three-dimensional model using the first feature vector. First foreground image data comprising a two-dimensional representation of the transformed representation of the three-dimensional model may be generated. A frame of composite image data may be generated by combining the first foreground image data with the first background image data.

Generation of synthetic image data using three-dimensional models

Devices and techniques are generally described for estimating three-dimensional pose data. In some examples, a first machine learning network may generate first three-dimensional (3D) data representing input 2D data. In various examples, a first 2D projection of the first 3D data may be generated. A determination may be made that the first 2D projection conforms to a distribution of natural 2D data. A second machine learning network may generate parameters of a 3D model based at least in part on the input 2D data and based at least in part on the first 3D data. In some examples, second 3D data may be generated using the parameters of the 3D model.

Three-dimensional pose estimation

Devices and techniques are generally described for weakly-supervised object segmentation in image data. In various examples, a first frame of image data may be received. The first frame may include a first bounding box surrounding a first set of pixels, wherein first subset of pixels of the first set of pixels represent a first object of a first class and wherein second subset of pixels of the first set of pixels represent background image data. Cross-entropy loss may be determined for the first set of pixels. In some examples, a spatial attention map may be determined for the first set of pixels. In further examples, parameters of a convolutional neural network may be determined by modulating the cross-entropy loss for the first set of pixels using the spatial attention map. The convolutional neural network may be used to generate a segmentation map.

Segmentation using attention-weighted loss and discriminative feature learning

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

Growing community of inventors

Santa Clara, CA, United States of America

Amit Kumar Agrawal