PATENT DOCUMENT

Publication Number: US-11394898-B2
Application Number: US-201816124168-A
Country: US
Kind Code: B2

Title: Augmented reality self-portraits

Abstract:
Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, live image data, the live image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.

Claims:
What is claimed is: 
     
       1. A method comprising:
 presenting a preview on a display of a mobile device, the preview including first frames of preview video data captured by a forward-facing camera of a mobile device, the first frames of the preview video data including video data of a user and a background behind the user in a physical, real-world environment; 
 receiving a first user input to apply a virtual environment effect; 
 capturing, by the forward-facing camera, second frames of video data; 
 capturing, by one or more sensors of the mobile device, (i) depth data indicating a distance of the user from the forward-facing camera in the physical, real-world environment and (ii) orientation data indicating an orientation of the forward-facing camera in the physical, real-world environment; 
 generating, by one or more processors of the mobile device, a camera transform based on the orientation data, the camera transform describing an orientation of a virtual camera in a virtual environment; 
 generating, by the one or more processors, a matte from the second frames of video data and the depth data; 
 receiving, by the one or more processors, second input indicating that third frames of video data are being captured by a second camera of the mobile device that is different than the forward-facing camera of the mobile device; 
 responsive to the second input, adjusting, by the one or more processors, the camera transform; 
 obtaining, by the one or more processors and using the adjusted camera transform, a virtual background content; and 
 generating, by the one or more processors, composite frames of video data that include the third frames of video data, the matte and the virtual background content. 
 
     
     
       2. The method of  claim 1 , further comprising:
 detecting, by the one or more sensors of the mobile device, new orientation data indicating a change in the orientation of the forward-facing camera in the physical, real-world environment; 
 generating an updated camera transform; 
 obtaining an updated virtual background content based on the updated camera transform; 
 generating updated composite frames of video data based on the updated camera transform; and 
 causing preview display of the updated composite frames of video data on the display of the mobile device. 
 
     
     
       3. A mobile device comprising:
 a display; 
 a forward-facing camera; 
 one or more sensors; 
 one or more processors; and 
 memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
 presenting a preview on the display, the preview including first frames of preview video data captured by the forward-facing camera, the first frames of preview video data including video data of a user and a background behind the user in a physical, real world environment; 
 receiving a first user input to apply a virtual environment effect; 
 capturing, by the forward-facing camera, second frames of video data; 
 capturing, by the one or more sensors, (i) depth data indicating a distance of the user from the forward-facing camera in the physical, real-world environment and orientation data indicating an orientation of the forward-facing camera in the physical, real-world environment; 
 generating a camera transform based on the orientation data, the camera transform describing an orientation of a virtual camera in a virtual environment; 
 generating a matte from the second frames of image data and the depth data; 
 receiving second input indicating that third frames of video data are being captured by a second camera of the mobile device that is different than the forward-facing camera of the mobile device; 
 responsive to the second input, adjusting the camera transform; 
 obtaining, using the adjusted camera transform, a virtual background content; and 
 generating composite frames of video data that include the third frames of video data, the matte and the virtual background content. 
 
 
     
     
       4. The mobile device of  claim 3 , the operations further comprising:
 detecting, by the one or more sensors of the mobile device, new orientation data indicating a change in the orientation of the forward-facing camera in the physical, real-world environment; 
 generating an updated camera transform; 
 obtaining an updated virtual background content based on the updated camera transform; 
 generating updated composite frames of video data based on the updated camera transform; and 
 causing preview display of the updated composite frames of video data on the display.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to U.S. Provisional Patent Application No. 62/556,297, entitled “Augmented Reality Self-Portraits,” filed on Sep. 8, 2017, the entire contents of which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to media editing and augmented reality. 
     BACKGROUND 
     Self-portrait digital photos or “selfies” have become a pop-culture phenomenon. Selfies are typically taken with a digital camera or smart phone held at arm&#39;s length, pointed at a mirror or attached to a selfie stick to position the camera farther away from the subject and capture the background scene behind the subject. Selfies are often shared on social networking services (e.g., Facebook®, Instagram®, Twitter®). Augmented reality (AR) is a live view of a physical, real-world environment whose elements are “augmented” by computer-generated sensory input such as sound, video or graphics. 
     SUMMARY 
     Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” 
     In an embodiment, a method comprises: capturing, by a first camera of a mobile device, live image data, the live image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating, by one or more processors of the mobile device, a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; receiving, by the one or more processors, content from the virtual environment; generating, by the one or more processors, a matte from the image data and the depth data; generating, by the one or more processors, a composite image data, using the image data, the matte and first virtual background content, the first virtual background content selected from the virtual environment using the camera transform; and causing display, by the one or more processors, the composite image data on a display of the mobile device. 
     In an embodiment, a method comprises: presenting a preview on a display of a mobile device, the preview including sequential frames of preview image data captured by a forward-facing camera of a mobile device positioned in close range of a subject, the sequential frames of preview image data including close range image data of the subject and image data of a background behind the subject in a physical, real world environment; receiving a first user input to apply a virtual environment effect; capturing, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the forward-facing camera in the physical, real-world environment; capturing, by one or more sensors of the mobile device, orientation data indicating at least an orientation of the forward-facing camera in the physical, real-world environment; generating, by one or more processors of the mobile device, a camera transform based on the motion data, the camera transform describing an orientation of a virtual camera in a virtual environment; obtaining, by the one or more processors and using the camera transform, a virtual background content from the virtual environment; generating, by the one or more processors, a matte from the sequential frames of image data and the depth data; generating, by the one or more processors, composite sequential frames of image data, including the sequential frames of image data, the matte and the virtual background content; and causing display, by the one or more processors, of the composite sequential frames of image data. 
     Other embodiments are directed to systems, method, apparatuses and non-transitory, computer-readable mediums. 
     Particular implementations disclosed herein provide one or more of the following advantages. The user experience of creating a selfie on a mobile device is improved by allowing the user to capture and record a selfie video using a forward-facing or reverse-facing camera embedded in the mobile device, and automatically replace the real-world background captured in a live video preview user-selected virtual background content that automatically updates in response to motion data from motion sensors of the mobile device. The disclosed implementations therefore provide an interactive and entertaining process for capturing selfie images that can be shared with friends and family through social networks. 
     The details of the disclosed implementations are set forth in the accompanying drawings and the description below. Other features, objects and advantages are apparent from the description, drawings and claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a conceptual drawing illustrating the basic concept of an AR selfie, according to an embodiment. 
         FIGS. 2A-2E  illustrate mapping of a virtual environment to a mobile device viewport, according to an embodiment. 
         FIGS. 3A and 3B  illustrate a graphical user interface for recording AR selfies using a forward-facing camera, according to an embodiment. 
         FIGS. 3C and 3D  illustrate graphical user interfaces with different background scenes selected and showing a recording view and full-screen playback view, according to an embodiment. 
         FIGS. 3E and 3F  illustrate graphical user interfaces for recording and playing back selfies using a backward-facing camera and showing a recording view and full-screen playback view, according to an embodiment. 
         FIG. 4  is a block a diagram of a system illustrating the process steps used in the creation of an AR selfie, according to an embodiment. 
         FIG. 5  illustrates compositing layers used in an AR selfie, according to an embodiment. 
         FIGS. 6A-6L  illustrate a multi-stage process for generating a preprocessed (coarse) matte using depth data, according to an embodiment. 
         FIGS. 7A-7C  illustrate a refined matting process using video data and the preprocessed (coarse) matte, according to an embodiment. 
         FIG. 8  illustrates a post-processing stage to remove artifacts from the refined matte, according to an embodiment. 
         FIG. 9  is a flow diagram of a process for generating an AR selfie, according to an embodiment. 
         FIG. 10  is a flow diagram of a process for generating an AR selfie matte, according to an embodiment. 
         FIG. 11  illustrates device architecture for implementing the features and process described in reference to  FIGS. 1-10 , according to an embodiment. 
     
    
    
     The same reference symbol used in various drawings indicates like elements. 
     DETAILED DESCRIPTION 
     A “selfie” is a self-portrait image taken by a user, often in close proximity by holding a camera within arms-length or using an extension device, such as a “selfie” stick. The selfie subject is often of the user&#39;s face, or a portion of the user (e.g., the user&#39;s upper body) and any background visible behind the user. A forward-facing camera is a camera that is facing the user as they are viewing the display screen. Alternatively, a backward-facing camera is facing away from the user as they are viewing the display screen, and captures images of the real-world environment in front of, and in the opposite direction, of the user. A typical mobile device for capturing selfies is a digital camera, a smart phone with one or more embedded digital cameras or a tablet computer with one or more embedded cameras. 
     In an embodiment, a selfie subject can be composited with virtual background content extracted from a virtual environment data model. The virtual background content can include but is not limited to: a two-dimensional (2D) image, a three-dimensional (3D) image and 360° video. In a preprocessing stage, a coarse matte is generated from depth data provided by a depth sensor and then refined using video data (e.g., RGB video data). In an embodiment, the depth sensor is an infrared (IR) depth sensor embedded in the mobile device. The matte is composited (e.g., using alpha compositing) with the video data containing an image of the selfie subject, and the real-world background behind the subject is replaced and continuously updated with virtual background content selected from a virtual environment selected by the user. The virtual background content is selected using a virtual camera transform generated using motion data from one or more motion sensors of the mobile device (e.g., accelerometers, gyroscopes). The video data, refined matte, virtual background content and optionally one or more animation layers are composited to form an AR selfie video. The AR selfie video is displayed to the user by a viewport of the mobile device. 
     In an embodiment, the mobile device also includes a backward-facing camera that can be used to capture video in front of the user, and which can be processed in a similar manner as video captured the forward-facing camera. A camera flip signal provided by the operating system of the mobile device can indicate, which camera is capturing video, and that signal can be used to adjust the virtual camera transform to update the virtual background content. 
     A matte generation process is disclosed that uses undefined depth data (also referred to herein as “shadow data”) to segment a depth image (e.g., binary depth matte) into foreground and background regions. The matte contains coverage information that includes a contour of the subject being drawn, making it possible to distinguish between parts of the binary depth matte where the subject was actually drawn and other parts of the binary depth matte that are empty. In an embodiment, the matte generation process uses a region-growing algorithm and/or a 3D face mesh to identify and fill “holes” (undefined depth data) in the matte caused by sunlight reflecting off sunglasses worn by the subject. 
     Although the matte generation process is disclosed herein as part of an AR selfie generation process, the disclosed matte generation process can be used to generate mattes from depth data for any image processing application. For example, the disclosed matte generation process can be used to segment images as a part of a video/image editing tool. 
     In an embodiment, the virtual environment can be any desired environment, such as a famous city (e.g., London, Paris or New York), and include famous landmarks (e.g., Big Ben, London Bridge, Eifel Tower). The virtual environment can also be completely fictional, such as a cartoon environment complete with cartoon characters, flying saucers and any other desired props. In an embodiment, motion effects (e.g., blurring effects, glowing effects, cartoon effects) can be applied to one or more of the video data, the virtual background content and the matte. Motion effects can also be applied to the final composite video. In an embodiment, one or more animation layers (e.g., a layer of animated particles resembling snow falling or sparks) can be composited with the video data, the matte and the virtual background content. 
     In an embodiment, a selfie GUI includes various controls, such as a control for recording an AR selfie video to a storage device (e.g., flash memory of the mobile device), a control for turning one or more microphones of the mobile device on and off, a camera reversal button for switching between forward-facing and backward-facing cameras and a tray for storing thumbnail images of AR selfie videos that can be selected to retrieve and playback the corresponding video on the mobile device. 
     Overview of AR Selfie Concept 
       FIG. 1  is a conceptual drawing illustrating the concept of an AR selfie, according to an embodiment. User  100  is shown taking a selfie using a forward-facing camera of mobile device  102 . During recording, a viewport on mobile device  102  displays a live video feed of user  100  in the foreground with virtual background content  104  extracted from virtual environment  106 . When user  100  changes the orientation of mobile device  102  in the real-world (e.g., rotates the view direction of the camera), motion sensors (e.g., accelerometers, gyros) of mobile device  102  sense the change and generate motion data that is used to update virtual background content  104  with new virtual background content extracted from a different portion of virtual environment  106 , as described further in reference to  FIGS. 2A-2E . The portion extracted from virtual background content  104  depends on how user  100  is holding mobile device  102 . For example, if user  100  is holding mobile device  102  in “portrait” orientation when taking a selfie, then the portion extracted from virtual background content  104  will have an aspect ratio that will fill the viewport in a portrait or vertical orientation. Similarly, if user  100  is holding mobile device  102  in “landscape” orientation when taking a selfie, then the portion extracted from virtual background content  104  will have an aspect ratio that will fill the viewport in a landscape or horizontal orientation. 
     Example Mapping of a Virtual Environment 
       FIGS. 2A-2E  illustrate mapping of a virtual environment to a viewport of a mobile device, according to an embodiment.  FIG. 2A  shows unit sphere  106  with corners of viewport  202  ( FIG. 2C ) projected onto its surface.  FIG. 2B  shows an equirectangular projection  200  (e.g., a Mercator projection) that is generated by mapping the projected viewport  202  from a spherical coordinate system to a planar coordinate system. In an embodiment, the horizontal line dividing equirectangular projection  200  is the equator of unit sphere  106  and the vertical line dividing equirectangular projection  200  is the prime meridian of unit sphere  106 . The width of equirectangular projection  200  spans from 0° to 360° and the height spans 180°. 
       FIG. 2C  shows subrectangle  203  overlying equirectangular projection  200 . Subrectangle  203  represents viewport  202  of mobile device  102  in planar coordinates. Equirectangular projection  200  can be sampled into viewport  202  using Equations [1] and [2] with reference to  FIG. 2E : 
     
       
         
           
             
               
                 
                   
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       FIG. 2D  illustrates mobile device  102  with viewport  202  and forward-facing camera  204 . A viewing coordinate system (X c , Y c , Z c ) is shown where the +Z c  coordinate is the forward-facing camera&#39;s view direction. In computer graphics, a camera analogy is used where viewer  206  located at a view reference point (VRP) observes a virtual environment through virtual camera  205  and can look and move around the virtual environment. This is accomplished by defining a viewing coordinate system (VCS) which has the position and orientation of virtual camera  205 , as shown in  FIGS. 2D and 2E . In  FIG. 2E , virtual camera  205  is shown as fixed position to the origin and has a latitude (ϕ) and longitude (λ) in a virtual world coordinate system. One can imagine virtual camera  205  looking out at unit sphere  106  with an image of the virtual backward-facing camera in the −Z direction as shown in  FIG. 2D . For forward-facing camera  204 , virtual camera  205  is rotated by 180° (about the Y axis in  FIG. 2D ) to generate a forward-facing camera view in the +Z direction, which shows the virtual background “over the shoulder” of viewer  206 . 
     In an embodiment, an attitude quaternion generated by an attitude processor of mobile device  102  can be used to determine the view direction of the backward-facing and forward-facing cameras. When viewer  206  rotates mobile device  102 , the motion sensors (e.g., gyroscopes) sense the rotation or rotation rate and update the attitude quaternion of mobile device  102 . The updated attitude quaternion (e.g., a delta quaternion) can be used to derive a camera transform for determining the camera view direction in the virtual environment for a backward-facing camera, or can be further transformed by 180° for determining the camera view direction in the virtual environment for a forward-facing camera. 
     The mathematics for deriving the camera transform are well-known in computer graphics and will not be discussed further herein. An important feature of the disclosed embodiments, however, is that the real-world orientation of the real-world cameras are used to drive the orientation of the virtual camera in the virtual environment, the result being that as the view direction of the real-world camera changes in real-time, the virtual camera view direction (represented by the camera transform) also changes in sync with the real-world camera. As will be described below, this technique creates the illusion that the user is taking a selfie in virtual environment  106  ( FIG. 1 ), and therefore capturing the virtual background behind the user rather than the real-world background. In an embodiment, when a user first enters the scene the device orientation (e.g., azimuth, elevation) can be biased towards a portion of the scene that is visually impressive (referred to as a “hero angle”). For example, a delta can be applied to the device orientation when the user looks around the scene, with the delta calculated as the difference between the hero angle and the device orientation when the user enters the scene. 
     Example GUIs for Recording AR Selfies 
       FIGS. 3A and 3B  are graphical user interfaces for recording AR selfies, according to an embodiment. Referring to  FIG. 3A , AR selfie GUI  300  includes viewport  301  displaying a composite video frame that includes selfie subject  302   a  and virtual background content  303   a . A “cartoon” special effect has been applied to the composite video to create an interesting effect and to hide artifacts from the alpha compositing process. Although a single composite video frame is shown, it should be understood that viewport  301  is displaying a live video feed (e.g., 30 frames/second), and if the orientation of the real-world camera view direction changes, virtual background  303   a  will also seamlessly change to show a different portion of the virtual environment. This allows the user to “look around” the visual environment by changing the view direction of the real-world camera. 
     In an embodiment, the location of the virtual camera, in addition to its orientation, can be changed in the virtual environment. For example, the location of the virtual camera can be changed by physically moving the mobile device or by using an GUI affordance (a virtual navigation button). In the former, location data (e.g., GNSS data) and/or inertial sensor data (e.g., accelerometer data) can be used to determine the position of the virtual camera in the virtual environment. In an embodiment, the virtual environment can be 3D video, 3D 360° video or 3D computer-generated imagery (CGI) that can respond to a user&#39;s actions. 
     GUI  300  also includes several affordances for performing various tasks. Tab bar  304  allows the user to select a photo editing option, such as invoking AR selfie recording. Tab bar  305  allows the user to select a camera function (e.g., photo, video, panorama, library). Tab bar  304  can be context sensitive such that the options in tab bar  304  can change based on the camera function that is selected in tab bar  305 . In the example shown, the “video” option is selected in tab bar  305  and the AR selfie recording option  311  is selected in tab bar  304 . 
     To record the AR selfie, GUI  300  includes virtual record button  306  for recording the AR selfie to local storage (e.g., flash memory). Thumbnail image tray  309  can hold thumbnail images for recorded AR selfies, which can be selected to playback the corresponding AR selfie video in viewport  301 . Camera reversal button  307  allows the user to toggle between forward-facing and backward-facing cameras. Microphone enable button  308  toggles one or more microphones of mobile device  102  on and off. A done button  310  exits GUI  300 . 
       FIG. 3B  shows a different special effect applied to selfie subject  302   b  and a different virtual background content  303   b . For example, virtual background content can be a cartoon environment with animated cartoon characters and other objects. It should be understood that any virtual background content can be used in an AR selfie. In some implementations, animated objects (e.g., animated particles such as snowflakes and sparks) can be inserted between the selfie subject and the virtual background content to create a more beautiful virtual environment, as described in reference to  FIG. 5 . In an embodiment, selfie subject  302   b  can be given an edge treatment, such as a “glow” or outline around the image or an “ink” outline. In an embodiment, animated objects can be inserted in front of selfie subjects  302   a ,  302   b . For example, selfie subjects  302   a ,  302   b  can be surrounded by a floating text ribbon or other animated object. In an embodiment, selfie subjects  302   a ,  302   b  can be layered over an existing real-world photo or video. 
       FIGS. 3C and 3D  illustrate graphical user interfaces with different background scenes selected and showing a recording view and full-screen playback view, according to an embodiment. In  FIG. 3C , a recording view is shown where user  302   c  has selected a virtual background  303   c . Note that during recording, viewport  301  is not full-screen to provide room for recording controls. In  FIG. 3D , a full-screen playback view includes scene selector  313  that can be displayed when user  302   d  has selected the “SCENES” affordance  312 . In an embodiment, scene selector  313  is a touch control that can be swiped by user  302   d  to select virtual background  303   d , which in this example is a Japanese tea garden. Also note that virtual background  303   d  is now displayed full-screen in viewport  311 . 
       FIGS. 3E and 3F  illustrate graphical user interfaces for recording and playing back selfies using a backward-facing camera and showing a recording view and full-screen playback view, according to an embodiment. In  FIG. 3E , a recording view is shown with virtual background  303   e . Virtual background  303   e  is what a user would see in front of them through the backward-facing camera in the virtual environment. Affordance  307  can be selected by the user to toggle between forward-facing and backward-facing cameras. In  FIG. 3F , a full-screen playback view includes scene selector  313  that can be displaced when user  302   d  has selected the “SCENES” affordance  312 . In an embodiment, scene selector  313  can be swiped by user  302   d  to select virtual background  303   f , which in this example is a Japanese tea garden. Also note that virtual background  303   f  is now displayed full-screen in viewport  314 . In an embodiment, when the user first selects a virtual environment a pre-defined orientation is presented in the viewport. 
     Example System for Generating AR Selfies 
       FIG. 4  is a block a diagram of system  400  illustrating the processing steps used in the creation of an AR selfie, according to an embodiment. System  400  can be implemented in software and hardware. Forward-facing camera  401  generates RGB video and IR depth sensor  402  generates depth data, which are received by Audio/Visual (A/V) processing module  403 . A/V processing module  403  includes software data types and interfaces to efficiently manage queues of video and depth data for distribution to other processes, such as matting module  409 , which performs the processes described in reference to  FIGS. 6A-6L . A/V processing module  403  also provides foreground video  404  including images of the selfie subject, which can be optionally processed with a motion effect  405   a , such as the “cartoon” effect shown in  FIG. 3A . Matting module  409  outputs a foreground alpha matte  410 , which can be optionally processed by motion effect module  405   b.    
     For virtual background processing, one or more of 2D image source  411 , 3D image source  412  or 360° video source  413  can be used to generate virtual background content  415 . In an embodiment, a 3D image source can be a rendered 3D image scene with 3D characters. These media sources can each be processed by motion source module  412 , which selects the appropriate source depending the virtual environment selected by the user. Motion compositing module  406  generates composite video from foreground video  404 , foreground alpha matte  410  and virtual background content  415 , as described in reference to  FIG. 5 . Motion effect  407  (e.g., a blurring effect) can be optionally applied to the composite video output by motion compositing module  406  to generate the final AR selfie  408 . 
     Accelerometer and gyroscope sensors  416  provide motion data that is processed by motion processing module  417  to generate a camera transform, as described in reference to  FIGS. 2A-2E . During recording, live motion data from the sensors  416  is used to generate the AR selfie and is stored in a local storage device (e.g., stored in flash memory). When the AR selfie is played back, the motion data is retrieved from the local storage device. In an embodiment, in addition to virtual camera orientation, virtual camera position in the virtual environment can be provided by motion processing module  417  based on sensor data. With virtual camera and position information, the user can walk around the 3D scene with 3D characters. 
     Example Compositing Process 
       FIG. 5  illustrates compositing layers used in an AR selfie, according to an embodiment. In an embodiment, alpha compositing is used to combine/blend the video data containing an image of the selfie subject with the virtual background content. An RGB-Depth matte (“RGB-D matte”) includes contour information for the subject projected on a binary depth matte, which is used to combine the foreground image of the subject with the virtual background content. 
     In the example shown, one or more animation layers  502  (only one layer is shown) is composited on background content  501 . Matte  503  is composited on one or more animation layers  502  and foreground RGB video data  504 , including the subject is composited on matte  503 , resulting in the final composite AR selfie, which is then displayed through viewport  301  presented on a display of mobile device  102 . In an embodiment, a motion effect can be applied to the composite video, such as a blurring effect to hide any artifacts resulting from the compositing process. In an embodiment, animation layers can be composited in front or back of the RGB video data  504 . 
     Example Processes for Generating RGB-D Matte 
     In an embodiment, the depth sensor is an IR depth sensor. The IR depth sensor includes an IR projector and an IR camera, which can be an RGB video camera that operates in the IR spectrum. The IR projector projects a pattern of dots using IR light which falls on objects in the image scene, including the subject. The IR camera sends a video feed of a distorted dot pattern into a processor of the depth sensor and the processor calculates depth data from the displacement of the dots. On near objects the pattern of dots is dense and on far objects the pattern of dots are spread out. The depth sensor processor builds a depth image or map that can be read from by a processor of a mobile device. If the IR projector is offset from the IR camera, some of the depth data may be undefined. Typically, this undefined data is not used. In the disclosed matte generation process, however, the undefined data is used to improve segmentation and contour detection, resulting in a more seamless composite. 
     Referring to  FIGS. 6A and 6B , matte generation process  600  can be divided into three stages: preprocessing stage  603 , RGB-D matting stage  604  and post-processing stage  605 . Process  600  takes as input RGB video data  601  that includes images of the subject and a depth map  602  that includes the depth data provided by the IR depth sensor. It should be observed that depth map  602  includes areas of shadow where the depth data is undefined. Note that the shadow along the left contour of the subject&#39;s face is thicker (more undefined data) than along the right contour of the subject&#39;s face. This is due to the offset between the IR projector and the IR camera. Each of stages  603 - 605  will be described in turn below. 
     Referring to  FIG. 6C , the steps of pre-processing stage  603  are shown, which include histogram generation  606 , histogram thresholding  607 , outer contour detection  608 , inner contour detection  609  and coarse depth matte generation  610 , iterative region growing  612  and a 3D face mesh modeling  613 . Each of these preprocessing steps will now be described in turn. 
     Histogram generation  606  places the depth data into bins. The histogram-thresholding step  607  is used to segment the foreground depth data from the background depth data by looking for “peaks and valleys” in the histogram. As shown in  FIG. 6D , histogram  614  is generated from absolute distance data, where the vertical axis indicates the number of depth data values (hereinafter called “depth pixels”) in each bin and the horizontal axis indicates the distance values provided by the depth sensor, which in this example is absolute distance. Note that in this example the distance values are in bin index multiples of 10. 
     It can be observed from  FIG. 6D , that the foreground pixels cluster together in adjacent bins centered around 550 mm, and the background pixels cluster together in adjacent bins centered around 830 mm. Note that there could be additional clusters of distance data if an object was inserted in between the subject and the background or in front of the subject. A distance threshold can be established (shown as line  615 ) that can be used to segment the pixels into foreground and background pixels based on distance to create a binary depth matte. For example, each pixel that has a distance less than 700 mm is designated as foreground and assigned a binary value of 255 for white pixels in the binary depth matte (e.g., assuming an 8-bit matte), and each pixel that has a distance greater than 700 mm is designated as background and is assigned a binary value of 0 for black pixels in the binary depth matte. 
     Referring to  FIG. 6E , threshold  615  (e.g., at about 700 mm) is applied to histogram  614  to generate two binary depth mattes  616   a ,  616   b  for finding inner and outer contours of the subject, respectively. In an embodiment, threshold  615  can be selected to be the average distance between the outer most bin of the foreground bins (the bin containing pixels with the longest distances) and the inner most bin of the background pixels (the bin containing pixels with the shortest distances). 
     Although the segmentation of pixels described above uses a simple histogram thresholding method, other segmentation techniques could also be used including but not limited to: balanced histogram thresholding, k-means clustering and Otsu&#39;s method. 
     Referring again to  FIG. 6E , steps  608 ,  609  extract the inner and outer contours of the subject from binary depth mattes  616   a ,  616   b , respectively. A contour detection algorithm is applied to depth mattes  616   a ,  616   b . An example contour detection algorithm is described in Suzuki, S. and Abe, K.,  Topological Structural Analysis of Digitized Binary Images by Border Following . CVGIP 30 1, pp. 32-46 (1985). 
     Depth matte  616   a  is generated using only defined depth data and depth matte  616   b  is generated using defined and undefined depth data (shadow data). If depth mattes  616   a ,  616   b  were to be combined into a single depth matte, the resulting combined depth matte would be similar to trimap  704  shown in  FIG. 7C , where the grey region (referred to as the “blended” region) between the inner and outer contours included undefined depth data which may include important contour detail that should be included in the foreground. After the inner and outer contours are extracted they can be smoothed using, for example, a Gaussian blur kernel. After the contours are smoothed, they are combined  618  into coarse depth matte  619 , as described in reference to  FIGS. 6F-6I . 
       FIG. 6F  illustrates the use of a distance transform to create coarse depth matte  619 . Outer contour  621  and inner contour  622  bound a blended region of undefined pixels (undefined depth data) between the contours. In some instances, some of the undefined pixels may include important contour information that should be assigned to the foreground (assigned white pixels). To generate coarse depth matte  619 , the subject is divided vertically into left and right hemispheres and a distance transform is performed on the undefined pixels in the blended region. 
     In an embodiment, perpendicular distances between pixels of inner contour  622  and outer contour  621  are calculated, as shown in  FIGS. 6F and 6G . Next, probability density functions of the calculated distances are computed separately for the left and right hemispheres, as shown in  FIGS. 6H and 6I . The left and right hemispheres have different probability density functions because, as noted earlier, the shadows on the left side of the subject&#39;s face are thicker than the shadows on the right side of the subject&#39;s face due to the offset between the IR projector and IR camera. In an embodiment, a Gaussian distribution model is applied to the distances to determine the mean μ and standard deviation a for each of the left and right hemispheres. The standard deviation a, or a multiple of the standard deviation (e.g., 2σ or 3σ), can be used as a threshold to compare against the distances in each hemisphere. The pixels in the undefined region (the grey region) in the left hemisphere are compared to the threshold for the left hemisphere. The pixels that have distances that are less than or equal to the threshold are included in the foreground and are assigned white pixel values. The pixels that have distances greater than the threshold are included in the background and are assigned black pixel values. The same process is performed for the right hemisphere. The result of the distance transform described above is coarse depth matte  619 , which concludes preprocessing stage  603 . 
     Example Region Growing/Face Mesh Processes 
     In some cases, the coarse matte  619  will have islands of undefined pixels in the foreground. For example, when a selfie is taken outdoors in the sunlight the performance of the IR depth sensor is degraded. In particular, if the selfie subject is wearing sunglasses, the resulting depth map will have two black holes where the eyes are located due to the sun&#39;s reflection off the sunglasses. These holes can be found in coarse depth matte  619  and filled with white pixels using an iterative region growing segmentation algorithm. In an embodiment, a histogram of foreground RGB video data  601  can be used to determine a suitable threshold value for region membership criterion. 
     Referring to  FIGS. 6J-6L , 3D face mesh model  625  can be generated from the RGB video data  623 . Face mesh model  625  can be used to identify the locations of facial landmarks on the subject&#39;s face, such as sunglasses  624 . Face mesh model  625  can be overlaid on coarse depth matte  626  to identify the location of sunglasses  624 . Any islands  628  of undefined pixels in foreground region  627  that are identified by face mesh model  625  are filled-in with white pixels so that the pixels are included in foreground region  627 . 
       FIGS. 7A and 7B  illustrate a process for RGB-D matting using a combination of RGB video data and the preprocessed depth matte  619 , according to an embodiment. Referring to  FIG. 7A , trimap module  701  generates trimap  704  from coarse depth matte  619 . In an embodiment, trimap module  704  uses the same segmentation process used to generate trimap  704  as used to generate coarse depth matte  619  or some other known segmentation technique (e.g., k-means clustering). Trimap  704  has three regions: a foreground region, a background region and a blended region. Trimap  704  is input into Gaussian Mixture Model (GMM)  702 , together with the RGB video data  601 . GMM  702  models the foreground and background regions (See  FIG. 7B ) by a probability density function approximated by a mixture of Gaussians, as shown in Equation [3]:
 
 p ( x |λ)=Σ i=1   M ω i   g ( x|μ   i ,Σ i ).  [3]
 
     The probability density function is used by graph cuts module  703  to perform segmentation using an iterative graph cuts algorithm. An example graph cuts algorithm is described in D. M. Greig, B. T. Porteous and A. H. Seheult (1989),  Exact maximum a posteriori estimation for binary images , Journal of the Royal Statistical Society Series B, 51, 271-279. The refined depth matte  705  output by graph cut module  703  is fed back into trimap module  701 , and the process continues for N iterations or until convergence. 
       FIG. 7C  shows the results of the previous two stages of matte generation process  600 . A depth map  602  is preprocessed into binary depth mattes  616   a ,  616   b , where depth matte  616   a  was generated using only defined depth data and depth matte  616   b  was generated using both defined and undefined depth data. Binary depth mattes  616   a ,  616   b  are then combined using a distance transform into coarse depth matte  619 . Coarse depth matte  619  is input to an RGB-D matting process  604  that uses an iterative graph cuts algorithm and a GMM to model foreground and background regions of the trimap  704 . The result of RGB-D matting process  604  is refined matte  705 . 
       FIG. 8  illustrates post-processing stage  605  to remove artifacts added by the refinement process, according to an embodiment. In post-processing stage  605 , distance transform module  803  calculates distances between the contours in coarse depth matte  619  and refined matte  705  using the same techniques as described in reference to  FIGS. 6F-6I . The distances are then compared to a threshold by distance check module  804 . Any undefined pixels that are farther than a threshold from the inner contour are deemed artifacts and assigned to the background region. In the example shown, depth matte  805  includes artifact  806  before post-processing. The end result of post-processing stage  606  is the final AR selfie matte  808  used for compositing the AR selfie, as described with reference to  FIG. 5 . Note that artifact  806  has be removed from AR selfie matte  808  due to the post-processing described above. 
     Example Processes 
       FIG. 9  is a flow diagram of process  900  for generating an AR selfie, according to an embodiment. Process  900  can be implemented using, for example, the device architecture described in reference to  FIG. 11 . 
     Process  900  can begin by receiving image data (e.g., video data) and depth data from an image capture device (e.g., a camera) and depth sensor ( 901 ), respectively. For example, the image data can be Red Green Blue (RGB) video data provided by an RGB video camera that includes an image of the subject. The depth sensor can be an IR depth sensor that provides a depth map that can be used to generate an RGB-Depth (“RGB-D”) matte, as described in reference to  FIG. 10 . 
     Process  900  continues by receiving motion data from one or more motion sensors ( 902 ). For example, motion data can be acceleration data and orientation data (e.g., angular rate data) provided by an accelerometer and gyroscope, respectively. The motion data can be provided in the form of a coordinate transform (e.g., a body-fixed quaternion). The coordinate transform describes the orientation of the camera&#39;s view direction in a real-world reference coordinate system, which can be transformed into a virtual world reference coordinate system using a camera transform. 
     Process  900  continues by receiving a virtual background content ( 903 ) from storage. For example, the virtual background content can be a 2D image, 3D image or 360° video. The virtual background content can be selected by the user through a GUI. The virtual background content can be extracted or sampled from any desired virtual environment, such as a famous city or cartoon environment with animated cartoon characters and objects. 
     Process  900  continues by generating a virtual camera transform from the motion data ( 904 ). 
     Process  900  continues by generating a matte from the image data and depth data ( 905 ). For example, an RGB-D matte can be generated as described in reference to  FIGS. 6I-6L . The RGB-D matte includes contour information for the subject and is use to compositing the RGB video with the virtual background content. 
     Process  900  can continue by compositing the image data, the RGB-D matte and the virtual background content ( 905 ), as described in reference to  FIG. 5 . During this step, the camera transform is used to extract or sample the appropriate virtual background content to composite with the image data and RGB-D matte ( 906 ). In an embodiment, one or more animation layers are also composited to provide, for example, animated particles (e.g., snowflakes, sparks, fireflies). In an embodiment, the camera transform is adjusted to account for camera flip caused by the user flipping between a forward-facing camera and a backward-facing camera and vice-versa, as described in reference to  FIG. 3A . 
     Process  900  can continue by rendering for display composite media (e.g., a composite video) in a viewport of the mobile device ( 907 ). During a recording operation, the composite media is presented as a live video feed. When the user changes the view direction of the real-world camera, the virtual camera transform updates in real-time the virtual background content in sync with the real-world camera. The recorded AR selfie video can be played back from storage through the viewport and also shared with others on, for example, on social networks. 
       FIG. 10  is a flow diagram of process  1000  for generating an AR selfie matte, according to an embodiment. Process  1000  can be implemented using, for example, the device architecture described in reference to  FIG. 11 . 
     Process  1000  can begin by generating a histogram of depth data ( 1001 ) and applying threshold(s) to the histogram to segment depth data into foreground and background regions ( 1002 ). 
     Process  1000  continues by generating outer and inner contours of the subject into binary depth mattes ( 1003 ). For example, an inner contour can be generated in a first binary depth matte using a contour detection algorithm and defined depth data only, and the outer contour can be generated in a second binary depth matte using the contour detection algorithm and depth data that includes both defined and undefined depth data. 
     Process  1000  continues by optionally smoothing the inner and outer contours ( 1004 ). For example, the inner and outer contours can be smoothed using a Gaussian blur kernel. 
     Process  1000  continues by combining the outer and inner contours to generate a coarse matte ( 1005 ). For example, a distance transform using a Gaussian distribution can be used to combine the first and second binary depth mattes into a combined coarse matte. 
     Process  1000  can continue by generating a refined matte (e.g., an RGB-D matte) using the coarse depth matte, the image data and the depth data ( 1006 ). For example, an iterative graphic cuts algorithm can be used on a trimap generated from the coarse matte and a GMM to generate the RGB-D matte. 
     Process  1000  can continue by removing undefined regions and artifacts from the refined matte ( 1007 ). For example, islands of undefined pixels in the foreground region of the RGB-D matte due to sunglasses reflecting sunlight can be identified and filled with white foreground pixels using an iterative region growing algorithm and/or a 3D face mesh model, as described in reference to  FIGS. 6J-6L . 
     Example Device Architecture 
       FIG. 11  illustrates a device architecture for implementing the features and process described in reference to  FIGS. 1-10 , according to an embodiment. Architecture  1100  can include memory interface  1102 , one or more data processors, video processors, co-processors, image processors and/or other processors  1104 , and peripherals interface  1106 . Memory interface  1102 , one or more processors  1104  and/or peripherals interface  1106  can be separate components or can be integrated in one or more integrated circuits. The various components in architecture  1100  can be coupled by one or more communication buses or signal lines. 
     Sensors, devices and subsystems can be coupled to peripherals interface  1106  to facilitate multiple functionalities. For example, one or more motion sensors  1110 , light sensor  1112  and proximity sensor  1114  can be coupled to peripherals interface  1106  to facilitate motion sensing (e.g., acceleration, rotation rates), lighting and proximity functions of the mobile device. Location processor  1115  can be connected to peripherals interface  1106  to provide geopositioning and process sensor measurements. In some implementations, location processor  1115  can be a GNSS receiver, such as a Global Positioning System (GPS) receiver chip. Electronic magnetometer  1116  (e.g., an integrated circuit chip) can also be connected to peripherals interface  1106  to provide data that can be used to determine the direction of magnetic North. Electronic magnetometer  1116  can provide data to an electronic compass application. Motion sensor(s)  1110  can include one or more accelerometers and/or gyros configured to determine change of speed and direction of movement of the mobile device. Barometer  1117  can be configured to measure atmospheric pressure around the mobile device. 
     Camera subsystem  1120  and one or more cameras  1122  (e.g. forward-facing camera and backward-facing camera) for capturing digital photographs and recording video clips, include videos and images used for generating an AR selfie, as described in reference to  FIGS. 1-10 . 
     Communication functions can be facilitated through one or more wireless communication subsystems  1124 , which can include radio frequency (RF) receivers and transmitters (or transceivers) and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of the communication subsystem  1124  can depend on the communication network(s) over which a mobile device is intended to operate. For example, architecture  1100  can include communication subsystems  1124  designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi™ or Wi-Max™ network and a Bluetooth™ network. In particular, the wireless communication subsystems  1124  can include hosting protocols, such that the mobile device can be configured as a base station for other wireless devices. 
     Audio subsystem  1126  can be coupled to a speaker  1128  and a microphone  1130  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording and telephony functions. Audio subsystem  1126  can be configured to receive voice commands from the user. 
     I/O subsystem  1140  can include touch surface controller  1142  and/or other input controller(s)  1144 . Touch surface controller  1142  can be coupled to a touch surface  1146  or pad. Touch surface  1146  and touch surface controller  1142  can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface  1146 . Touch surface  1146  can include, for example, a touch screen. I/O subsystem  1140  can include a haptic engine or device for providing haptic feedback (e.g., vibration) in response to commands from a processor. 
     Other input controller(s)  1144  can be coupled to other input/control devices  1148 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port and/or a pointer device such as a stylus. The one or more buttons (not shown) can include an up/down button for volume control of speaker  1128  and/or microphone  1130 . Touch surface  1146  or other controllers  1144  (e.g., a button) can include, or be coupled to, fingerprint identification circuitry for use with a fingerprint authentication application to authenticate a user based on their fingerprint(s). 
     In one implementation, a pressing of the button for a first duration may disengage a lock of the touch surface  1146 ; and a pressing of the button for a second duration that is longer than the first duration may turn power to the mobile device on or off. The user may be able to customize a functionality of one or more of the buttons. The touch surface  1146  can, for example, also be used to implement virtual or soft buttons and/or a virtual touch keyboard. 
     In some implementations, the mobile device can present recorded audio and/or video files, such as MP3, AAC and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player. Other input/output and control devices can also be used. 
     Memory interface  1102  can be coupled to memory  1150 . Memory  1150  can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices and/or flash memory (e.g., NAND, NOR). Memory  1150  can store operating system  1152 , such as iOS, Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. Operating system  1152  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, operating system  1152  can include a kernel (e.g., UNIX kernel). 
     Memory  1150  may also store communication instructions  1154  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers, such as, for example, instructions for implementing a software stack for wired or wireless communications with other devices. Memory  1150  may include graphical user interface instructions  1156  to facilitate graphic user interface processing; sensor processing instructions  1158  to facilitate sensor-related processing and functions; phone instructions  1160  to facilitate phone-related processes and functions; electronic messaging instructions  1162  to facilitate electronic-messaging related processes and functions; web browsing instructions  1164  to facilitate web browsing-related processes and functions; media processing instructions  1166  to facilitate media processing-related processes and functions; GNSS/Location instructions  1168  to facilitate generic GNSS and location-related processes and instructions; and camera instructions  1170  to facilitate camera-related processes and functions for forward-facing and backward-facing cameras. 
     Memory  1150  further includes media player instructions  1172 , and orientation-based, media presentation instructions  1174  for performing the features and processes described in reference to  FIGS. 1-10 . The memory  1150  may also store other software instructions (not shown), such as security instructions, web video instructions to facilitate web video-related processes and functions and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, the media processing instructions  1166  are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory  1150  can include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
     The described features can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language (e.g., SWIFT, Objective-C, C#, Java), including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, a browser-based web application, or other unit suitable for use in a computing environment. 
     Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors or cores, of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits). 
     To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor or a retina display device for displaying information to the user. The computer can have a touch surface input device (e.g., a touch screen) or a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer. The computer can have a voice input device for receiving voice commands from the user. 
     The features can be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a LAN, a WAN, and the computers and networks forming the Internet. 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server. 
     A system of one or more computers can be configured to perform particular actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. 
     One or more features or steps of the disclosed embodiments may be implemented using an Application Programming Interface (API). An API may define on or more parameters that are passed between a calling application and other software code (e.g., an operating system, library routine, function) that provides a service, that provides data, or that performs an operation or a computation. The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling convention that a programmer will employ to access functions supporting the API. In some implementations, an API call may report to an application the capabilities of a device running the application, such as input capability, output capability, processing capability, power capability, communications capability, etc. 
     As described above, some aspects of the subject matter of this specification include gathering and use of data available from various sources to improve services a mobile device can provide to a user. The present disclosure contemplates that in some instances, this gathered data may identify a particular location or an address based on device usage. Such personal information data can include location-based data, addresses, subscriber account identifiers, or other identifying information. 
     The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. 
     In the case of advertisement delivery services, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the content delivery services, or publically available information. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub combination or variation of a sub combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Metadata:
Filing Date: 20180906
Publication Date: 20220719
Grant Date: 20220719
Priority Date: 20170908
Inventors: WANG, XIAOHUAN CORINA
SUN, ZEHANG
WEIL, Joe
KHALILI, Omid
POMERANTZ, Stuart Mark
ROBINS, Marc
HORIE, TOSHIHIRO
BEALE, Eric
CASTEL, NATHALIE
BERTHOUD, JEAN-MICHEL
WALSH, BRIAN
O'NEIL, KEVIN
HARDING, Andy
DUDEY, GREG
Assignee: APPLE INC
CPC Classifications: [{"code": "H04N5/772", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/272", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/265", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/2226", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T15/503", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T11/60", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/194", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/174", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/11", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/631", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/6812", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/62", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/63", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/6812", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/631", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/63", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/62", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/90", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/772", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/2226", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/174", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/11", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T11/60", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10028", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/76", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20076", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/272", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/20221", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/194", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/265", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N5/2226", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/20", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/187", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/194", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/174", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/265", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T7/11", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T17/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/13", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/503", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/20", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T11/60", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/136", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/503", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/272", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/44504", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/772", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N23/951", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/951", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N23/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/005", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/232933", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T11/60", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/23229", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20076", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/136", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/13", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/265", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N5/76", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20221", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/23258", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/272", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/20", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/44504", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/772", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/23293", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/11", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/247", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N5/2226", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/187", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/23216", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T17/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/20", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/194", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/002", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/174", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/503", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/70", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/77", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 65631865