Qualcomm Incorporated

Hewlett-packard Company

Ning Bi

Yingyong Qi

Ying Hai Chen

Lei Wang

Michel Adib Sarkis

Xuerui Zhang

Feng Guo

Chienchung Chang

Ke-Li Cheng

Bijan Forutanpour

Jianfeng Ren

Andrew P Dejaco

Yang Zhou

Ruiduo Yang

Dashan Gao

Xin Zhong

Fan Ling

Jinglun Gao

Harinath Garudadri

Shuxue Quan

Kuang-Man Huang

Yan Deng

Phi Hung Nguyen

Shuai Zhang

Phi Hung Le Nguyen

Xuan Zou

Eyasu Zemene Mequanint

Lei Zhang

Amichai Sanderovich

Scott David Beith

Tao Zhang

Jian Wei

Evyatar Hemo

Chieh-Ming Kuo

Chun-Ting Huang

Ye-Kui Wang

Xinzhou Wu

Sharad Deepak Sambhwani

Zhen Wang

Milivoje M Aleksic

Lei Xu

Gerhard Reitmayr

Suhail Jalil

Yingen Xiong

Lin Chen

Evgeny Levitan

David Puig Oses

Sairam Sundaresan

Lingjun Chen

Chiranjib Choudhuri

Yushi Tian

Yang Yang

Hui Chao

Moinul H Khan

Nikolai Konrad Leung

Ananthapadmanabhan Arasanipalai Kandhadai

Gilbert Christopher Sih

Guofang Jiao

Lei Ma

Ajit Deepak Gupte

Nhon Toai Quach

Jason Borthwick Kenagy

Martin Renschler

Chinchuan Andrew Chiu

Narendranath Malayath

Ramesh Chandrasekhar

Jiajian Chen

Maurice Franklin Ribble

Karthik Nagarajan

Assaf Menachem

Eran Hof

Steven Douglas Laver

Matthew Fischler

Avani Rao

Anupama S

Ran-Fun Chiu

Bolan Jiang

Prasanjit Panda

William Yee-Ming Huang

Sichao Yang

Xin Li

Yunke Pan

Jiancheng Lyu

Yu Sun

Karthick Chinnaswami

Jingting Ding

Xiaoliang Bai

Kuen-Han Lin

Qing Zhou

George Gaozhi Pan

Naren Malayath

Zhongmin Wang

Shili Xu

Ronald Karyodisa

Binglong Xie

Systems and techniques are provided for generating a three-dimensional (3D) facial model. For example, a process can include obtaining at least one input image associated with a face. In some aspects, the process can include obtaining a pose for a 3D facial model associated with the face. In some examples, the process can include generating, by a machine learning model, the 3D facial model associated with the face. In some cases, one or more parameters associated with a shape component of the 3D facial model are conditioned on the pose. In some implementations, the 3D facial model is configured to vary in shape based on the pose for the 3D facial model associated with the face.

View dependent three-dimensional morphable models

Techniques and systems are provided for image processing. For instance, a process can include obtaining, from one or more image sensors, a first image of an environment; determining semantic labels for a plurality of pixels of the first image based on whether each pixel of the plurality of pixels is associated with an object in the first image to generate a semantically segmented image; applying a two-dimensional line representation of the first image to the semantically segmented image to generate a labeled 2D line representation; back-projecting the labeled 2D line representation to three dimensions to generate a line representation model; fusing the line representation model and the semantically segmented image to generate a labeled line representation model; and outputting the labeled line representation model.

Edge-based geometry representation for cross layer applications

Systems and techniques are provided for generating a texture for a three-dimensional (3D) facial model. For example, a process can include obtaining a first frame, the first frame including a first portion of a face. In some aspects, the process can include generating a 3D facial model based on the first frame and generating a first facial feature corresponding to the first portion of the face. In some examples, the process includes obtaining a second frame, the second frame including a second portion of the face. In some cases, the second portion of the face at least partially overlaps the first portion of the face. In some examples, the process includes combining the first facial feature with the second facial feature to generate an enhanced facial feature, wherein the combining is performed to enhance an appearance of select areas of the enhanced facial feature.

Facial texture synthesis for three-dimensional morphable models

Systems and techniques are described herein for generating one or more three-dimensional models. For instance, a method for generating one or more three-dimensional models is provided. The method may include obtaining a plurality of images of an object; obtaining a plurality of segmentation masks associated with the plurality of images, each segmentation mask of the plurality of segmentation masks including at least one label indicative of at least one segment of the object in a respective image of the plurality of images; training, using the plurality of images and the plurality of segmentation masks, a machine-learning model to generate one or more semantically-labeled three-dimensional models of the object; and generating using the trained machine-learning model, a semantically-labeled three-dimensional model of the object, the semantically-labeled three-dimensional model of the object including at least one label indicative of the at least one segment of the object.

Generating semantically-labelled three-dimensional models

Systems and techniques are provided for performing scene segmentation and object tracking. For example, a method for processing one or more frames. The method may include determining first one or more features from a first frame. The first frame includes a target object. The method may include obtaining a first mask associated with the first frame. The first mask includes an indication of the target object. The method may further include generating, based on the first mask and the first one or more features, a representation of a foreground and a background of the first frame. The method may include determining second one or more features from a second frame and determining, based on the representation of the foreground and the background of the first frame and the second one or more features, a location of the target object in the second frame.

Scene segmentation and object tracking

Systems and techniques are described for image processing. An imaging system receives an identity image and an attribute image. The identity image depicts a first person having an identity. The attribute image depicts a second person having an attribute, such as a facial feature, an accessory worn by the second person, and/or an expression. The imaging system uses trained machine learning model(s) to generate a combined image based on the identity image and the attribute image. The combined image depicts a virtual person having both the identity of the first person and the attribute of the second person. The imaging system outputs the combined image, for instance by displaying the combined image or sending the combined image to a receiving device. In some examples, the imaging system updates the trained machine learning model(s) based on the combined image.

Systems and methods for facial attribute manipulation

Methods, systems, and apparatuses are provided to automatically determine whether an image is spoofed. For example, a computing device may obtain an image, and may execute a trained convolutional neural network to ingest elements of the image. Further, and based on the ingested elements of the image, the executed trained convolutional neural network generates an output map that includes a plurality of intensity values. In some examples, the trained convolutional neural network includes a plurality of down sampling layers, a plurality of up sampling layers, and a plurality of joint spatial and channel attention layers. Further, the computing device may determine whether the image is spoofed based on the plurality of intensity values. The computing device may also generate output data based on the determination of whether the image is spoofed, and may store the output data within a data repository.

Apparatus and methods for spoofing detection using machine learning processes

Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

Object scanning using planar segmentation

Systems and techniques are provided for performing video-based activity recognition. For example, a process can include generating a three-dimensional (3D) model of a first portion of an object based on one or more frames depicting the object. The process can also include generating a mask for the one or more frames, the mask including an indication of one or more regions of the object. The process can further include generating a 3D base model based on the 3D model of the first portion of the object and the mask, the 3D base model representing the first portion of the object and a second portion of the object. The process can include generating, based on the mask and the 3D base model, a 3D model of the second portion of the object.

Object reconstruction using media data

Techniques and systems are provided for authenticating a user of a device. For example, input biometric data associated with a person can be obtained. A similarity score for the input biometric data can be determined by comparing the input biometric data to a set of templates that include reference biometric data associated with the user. The similarity score can be compared to an authentication threshold. The person is authenticated as the user when the similarity score is greater than the authentication threshold. The similarity score can also be compared to a learning threshold that is greater than the authentication threshold. A new template including features of the input biometric data is saved for the user when the similarity score is less than the learning threshold and greater than the authentication threshold.

User adaptation for biometric authentication

Examples are described of segmenting an image into image regions based on depicted categories of objects, and for refining the image regions semantically. For example, a system can determine that a first image region in an image depicts a first category of object. The system can generate a color distance map of the first image region that maps color distance values to each pixel in the first image region. A color distance value quantifies a difference between a color value of a pixel in the first image region and a color value of a sample pixel in a second image region in the image. The system can process the image based on a refined variant of the first image region that is refined based on the color distance map, for instance by removing pixels from the first image region whose color distances fall below a color distance threshold.

Semantic refinement of image regions

Systems and techniques are provided for facial expression recognition. In some examples, a system receives an image frame corresponding to a face of a person. The system also determines, based on a three-dimensional model of the face, landmark feature information associated with landmark features of the face. The system then inputs, to at least one layer of a neural network trained for facial expression recognition, the image frame and the landmark feature information. The system further determines, using the neural network, a facial expression associated with the face.

Facial expression recognition

Systems and techniques are provided for performing video-based activity recognition. For example, a process can include extracting, using a first machine learning model, first one or more features from a first frame and second one or more features from a second frame. The first one or more features and the second one or more features are associated with a person driving a vehicle. The process can include processing, using a second machine learning model, the first one or more features and the second one or more features. The process can include determining, based on processing of the first one or more features and the second one or more features using the second machine learning model, at least one activity associated with the person driving the vehicle.

Video-based activity recognition

A method for picture processing is described. A first tracking area is obtained. A second tracking area is also obtained. The method includes beginning to track the first tracking area and the second tracking area. Picture processing is performed once a portion of the first tracking area overlapping the second tracking area passes a threshold.

Tracker assisted image capture

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San Diego, CA, United States of America

Ning Bi