Patent Publication Number: US-2022215608-A1

Title: Personalized stylized avatars

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 16/363,640, filed Mar. 25, 2019, entitled “Personalized Stylized Avatars,” which is hereby incorporated by reference herein in its entirety. 
    
    
     FIELD 
     The present disclosure relates generally to generating avatars or other user representations for media, interactive content, and other applications. 
     BACKGROUND 
     Avatars or other graphic representations of users, characters, or objects are used in many applications, such as video games, interactive content, images, electronic communication (e.g., texting), instant messaging, and the like. Often, it is desirable to include individualized features on an avatar that allow a user to more closely resemble the avatar and allow others to identify a particular avatar as corresponding to a user, e.g., have a user&#39;s likeness. Additionally, in many instances, there is a desire to have avatars that match within a particular “style” or design framework (e.g., match character design elements). 
     Current avatar generation systems do not balance the competing factors of user likeness with design paradigms and select one factor over the other. For example, many video games will allow a user to vary certain colors of a character, but these colors are previously stored options and while may happen to match the user are not selected so as to match the user. In other examples, the avatars may be generated within a design paradigm, but do not resemble the user. In many instances, avatars need to be “manually” created, with a user actively selecting the particular avatar features that the user wishes to include on their own avatar, which is time consuming and often leads to inaccurate individual representations of a user. 
     SUMMARY 
     According to one embodiment, a method to generate user representative avatars that fit within a design paradigm is disclosed. The method includes receiving depth information corresponding to multiple user features of the user, determining one or more feature landmarks for the user based on the depth information, utilizing the one or more feature landmarks to classify a first user feature relative to an avatar feature category, selecting a first avatar feature from the avatar feature category based on the classification of the first user feature, combining the first avatar feature within an avatar representation to generate a user avatar, and output the user avatar for display. 
     According to another embodiment, a system for generating graphical representations of a person is disclosed. The system includes a camera for capturing images of the person and a computer in electronic communication with the camera. The computer includes a non-transitory memory component for storing a plurality of graphical features and a processor in electronic communication with the memory component. The processor is configured to perform operations including: receive the captured images of the person, determine from the captured images a plurality of facial landmarks for the person, utilize the plurality of facial landmarks to classify a first facial feature within a first graphical feature category and a second facial feature within a second graphical feature category, select a first graphical feature and a second graphical feature based on the classification, and combine the first graphical feature and the second graphical feature 
     According to yet another embodiment, a method to generate user-corresponding avatars is disclosed. The method includes, receiving by a processor depth information corresponding to multiple facial features of a user, detecting by analyzing the depth information by the processor, locations of the multiple facial features, quantizing by the processor, the facial features into a respective avatar facial feature type, selecting avatar facial feature elements to represent the user-corresponding avatar based on the respective facial feature type and the depth information, generate the user-corresponding avatar by combining the avatar facial elements together, and outputting by the processor to a display the user-corresponding avatar for display. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a perspective view of a system for generating personalized stylized avatars. 
         FIG. 2  is a simplified block diagram of the system of  FIG. 1 . 
         FIG. 3  is a flow chart illustrating a method to utilize the system of  FIG. 1  to generate personalized stylized avatars. 
         FIG. 4  is a flow chart illustrating a step of the method of  FIG. 3 . 
         FIG. 5  is an illustrative user image captured by the system of  FIG. 1 . 
         FIG. 6  is an image of a feature mask used during the method of  FIG. 3 . 
         FIG. 7  is an example of an avatar classification tree used by the method of  FIG. 3 . 
         FIG. 8A  is an example of a user depth information as captured by the system of  FIG. 1 . 
         FIG. 8B  is an example of a user mesh generated with obstructions and without obstructions. 
         FIG. 8C  is another example of the user mesh generated with and without obstructions. 
         FIG. 9A  is an example of a user mesh. 
         FIG. 9B  is an example of the wrapped user mesh. 
         FIG. 10  is an example of measurements for facial features and accessories. 
         FIG. 11  is a graphical illustration of a combination process to generate an avatar using the method and steps of  FIGS. 3 and 4 . 
         FIG. 12  is a personalized stylized avatar generated using the system of  FIG. 1 . 
     
    
    
     SPECIFICATION 
     The present disclosure is related to a method to generate personalized, stylized avatars that maintain a design paradigm, while also including individualized user likeness. The method includes capturing color and depth information for a user, such as via color images and/or depth scans of the user. Based on detected user information, user characteristics, such as facial and hair shapes, colors, and attributes (e.g., coarse hair, curly hair, etc.), are analyzed and classified or matched relative to a stored database of design paradigm features. For example, a hairline shape for the user is extracted from the user images and the shape is classified and matched to a selected paradigm shape, which is then used to build the user&#39;s avatar. As another example, the user&#39;s skin and eye color are extracted and classified within a paradigm scale to be matched to a paradigm selection of skin and eye color. In some instances the avatar selectable features are scaled or quantized such that the selected color/feature may not exactly match the user, but fits within the selected category of the desired character design. That is, the selected avatar features/colors may not be exact replicas of the user, but representations generally of user features/colors to correspond to the user but fit within the selected design environment. 
     In addition to the coloring and shape characteristics, the method also takes into account facial shape, structure, or other three-dimensional (3D) information. For example, using the detected user information, a geometric mesh corresponding to the user&#39;s facial features is generated. The 3D mesh is aligned and then wrapped over a generic 3D mesh (e.g., bio mesh), so that landmarks (e.g., nose, lips, eyes, ears) are aligned between the two meshes. The wrapping allows the user&#39;s features to be more accurately measured and grouped so as to be more fairly and accurately compared to corresponding avatar 3D features. In other words, the wrapped mesh may be scaled to the avatar features such that missing or inaccurate data from the user geometric mesh can be accommodated for and different user landmark measurements (e.g., nose eight, eye width, etc.) are analyzed accurately. 
     Using the wrapped mesh, the 3D landmarks are measured and compared or classified relative to selected or stored avatar or design features, where the stored avatar features are designed to match an avatar design paradigm. In one example, a user nose width spanning across X number of mesh vertices or X inches may be compared against paradigm nose sizes, such as small, medium, and large, where each nose size has a vertices or inches interval (e.g., 0.3 to 0.6 inches is a small nose, 0.65-0.9 is a medium nose, etc.). Once the nose or other 3D feature is classified, the corresponding avatar 3D feature size and shape is selected based on the classification and applied to the 3D mesh. The 3D features are applied to the wrapped mesh and the user specific built 3D mesh is combined with the shape and color features classified and a complete user specific avatar can be generated. 
     It should be noted that as used herein, the term “user” may encompass an operator of the system and/or software and/or a participant whose likeness is being used to generate the avatar. For example, in some instances, the user may be the subject of the personalized avatar, but may not operate or control the software. 
       FIG. 1  illustrates a perspective view of an avatar system  100  for generating personalized stylized avatars for users  106 .  FIG. 2  is a simplified block diagram of the system  100 . The system  100  includes a computer  102 , user capturing device  104 , such as a camera and/or depth sensor, and a display  110  for displaying an electronic representation of the user, such as an avatar  108 . The computer  102  and the user capturing device  104  are in communication with one another, such that data (e.g., images) can be transferred between the two devices and the capturing device  104  is in optical communication with a user  106  to allow the device to capture various information, such as images and depth information, of the user. The computer  102 , capturing device  104 , and display  108  may incorporated into a single device, be separate devices, or a combination of the two. The components may be in communication in various ways, e.g., direct wired communication, radio waves (e.g., WiFi, Bluetooth, etc.), or a combination of communication mechanisms. 
     The user capturing device  104  or camera is any device that can capture images of a user  106 , including visible and/or non-visible light. The camera  104  typically includes a lens and an image sensor. The type of image sensor and lens are typically varied based on the camera, as well as the type of user information to be captured (e.g., two-dimensional (2D) or 3D information). In many examples the camera  104  is captures color images that may be perceived by human vision. However, in other embodiments the camera may be configured to capture primary light channels outside the range of human visual perception. 
     The capture device  104  may include features to allow depth or 3D information detection for the user. For example, the capture device  104  may include two or more cameras positioned at different locations relative to the user  106  (e.g., active stereo methods), as well as a projector to project light (e.g., infrared light, collimated or laser light) onto the user  106  to detect reflection and other characteristics of the light as it reflects from the user  106 . Other depth sensors or depth sensing techniques may be used as well. The determined depth information may include topography information for the user&#39;s facial features, such as x, y, z points stored in a point cloud where the various points corresponds to the outer surface features of the user&#39;s face. 
     The capture or depth determining device may be varied depending on the implementation of the system. In one example, a light based sensor, such as infrared camera, or a light detection and ranging (LIDAR), ultrasound detectors, and other types system can be used. In one example, one or more high speed cameras can be used that takes multiple video frames per second (e.g., 1,000-3,000 and preferably around 2,000 fps) that can be to quickly map a user&#39;s 3D facial features. In another example, an artificial intelligent (AI) model can be used analyze 2D user images and estimate the user&#39;s 3D depth information. In this example, the AI model may be trained by a number of facial images and to estimate depth information that can be used to provide a unique avatar that is identifiable as a particular user. In many embodiments, the system can be configured to detect a minimum amount of depth information that allows the user to be uniquely identified within the avatar paradigm. 
     In another example, the capture device  104  may be an infrared sensor that includes an IR emitter and a sensor, where the emitter emits IR light and the sensor detects the reflectivity of the light from the subject. In this example, the user may be positioned in front of an IR absorbing background to enhance the detectability of the facial features. 
     The display  110  is any type of device that can display images, such as a liquid crystal display, organic light emitting diode display, plasma display, or the like. The display  110  should be configured to display animated still or moving images, such as those corresponding to the avatar  108 . The display  110  may be incorporated into a user device (e.g., tablet, smartphone, or the like), and/or a computational display to use for generation of the avatars  108  and that transmits the avatar  108  to a separate display for the user. 
     With reference to  FIG. 2 , the computer  102  may include a processing element  120 , input/output interface  122 , a power source  124 , and one or more memory components  126 , each of which, either individually or collectively, may be in communication with the display  110  and capture device  104 . 
     The processing element  120  or processor is substantially any electronic device capable of processing, receiving, and/or transmitting instructions, including a graphics processing unit, central processing unit, server, processor, or the like. The memory  126  stores electronic data used by the processor  120 , such as instructions to execute certain instructions, or the like. The memory  126  may store electrical data or content, such as, but not limited to, audio files, video files, document files, 3D user information, avatar paradigm classes, and the like. The memory  126  may be, for example, magneto-optical storage, read only memory, random access memory, erasable programmable memory, flash memory, or a combination of one or more types of memory components. 
     The input/output interface  122  provides communication to and from the computer  102 , the capture device  104 , and/or display  110 , as well as other devices. The input/output interface  122  can include one or more input buttons, a communication interface, such as WiFi, Ethernet, or the like, as well as other communication components such as universal serial bus (USB) cables, or the like. The power source  124  may be a battery, power cord, or other element configured to transmit power to the components of the projectors. 
       FIG. 3  illustrates a method  200  utilizing the system  100  to generate individualized user representations that fit within a design paradigm. The method  200  may begin with operation  202  and the user capture device  104  is used to capture user features. For example, the user capture device  104  captures one or more user images  304  (see  FIG. 5 ). In some embodiments, there may be multiple user images  304  captured, such as from different locations, viewpoints or the like. The number of user images  304  is selected based on the equipment to be sufficient to utilize photogrammetry techniques in order to measure landmarks and other locations on the user&#39;s face or other aspect being used for the avatar (e.g., legs, hands, etc.). In some instances, there may be 10-20 images and preferably 12 images that are captured of the user  106 . In embodiments where the avatar  108  includes colored aspects, the user images  302  include color information, such as the color of the user&#39;s  106  hair, eyes, skin tone, and the like. 
     The color information may be detected solely by the user capture device  104  or may be adjusted to account for abnormalities or environmental factors (e.g., lighting variations). For example, the images may be normalized to remove color aspects due to the environment lighting rather than the user&#39;s features. In some instances, the system may include arranged lighting sources to provide a more controlled environment for image capture, e.g., uniform lighting. 
     Additionally, in some embodiments, depth images or other depth characteristics are detected during this operation  202 . The depth information may be determined via the multiple user images  302  captured or may be completed as a separate step, such as a depth sensor or the use of other depth sensing techniques such as infrared and active stereo. 
     It should be noted that the user information captured in operation  202  may be destroyed or deleted after the avatar is completed, such that the system may not maintain records of the user information. The type and length of retention of the user information may be varied based on the implementation, user consent, and the like. 
     With reference again to  FIG. 3 , after operation  202 , the method  200  proceeds to operation  204  and landmarks for the user  106  are detected. For example, the processor  120  analyzes the user images  302  to determine locations and shapes of user features, e.g., face shape, hairline, ears  310 , eyes  314 , nose  308 , mouth  316 , glasses, etc. In one embodiment, computer vision techniques that compare pixel colors and locations in the user images  302  may be used to detect the desired user features. For example, computer vision techniques utilizing segmentation can be applied that use foreground and background extraction techniques to determine locations of certain facial features. Some embodiments may use color information to determine features, e.g., skin color versus hair color can be used to identify eyebrows, facial hair, etc., such as by using probability assessments. For example, the processor can analyze an image and determine based on color and/or location within the frame whether a particular pixel may be more likely to be a portion of the forehead or a portion of the eyebrow. In another example, an AI classifer can be used to conduct the image analysis and identify the facial features of users. To this end, the system may use algorithmic techniques, trained AI models, or the like, to determine the facial features. 
     In some embodiments, an orientation or alignment frame may be used to assist in capturing the user images, such that the user aligns their face within a “frame” displayed over an image of the user&#39;s face. This type of alignment may assist in expending the feature detection since locations of certain features may be likely to correspond to certain pixels or locations within the frame. 
     Using the identified features, feature masks  320 ,  322  may be generated in operation  204  that mask the outline of certain user features, such as a hairline mask  320  and eyebrow mask  322  that identify the location and perimeter of the facial or other features. The feature masks  320 ,  322  are generated based on a perimeter shape as identified during the detection of the user features. 
     After operation  204 , the method  200  may proceed to operations  206 ,  210 ,  216 , which may be completed in parallel or in series to one another. In operation  206 , the processor  120  uses the user images  304  to classify colors of the user features, such as the user&#39;s skin  306  color, eye color, hair color, eyebrow color, and the like. In one example, the processor  120  may conduct a pixel hue analysis around each of the locations determined to correspond to the selected landmark in operation  204 . For example, a select group of pixels may be identified in operation  204  as corresponding to the eyebrows  312  and the hue and brightness of the pixels can be compared to a color chart or lookup table corresponding to selected color buckets or quantized groups, e.g., color falling into a selected range of X-Y is classified as a first color and color falling into a selected range of A-B is classified as a second color. In another example, the color of the user feature may be classified based on a closest approximation, e.g., whatever color within the classification database that the color resembles may be selected. In other words, a best fit analysis may be used to classify the color or other feature for the user. In some embodiments, infrared sensors can be used to analyze color in the user images. In some instances, the user&#39;s hair may be identified as corresponding to two or more colors, e.g., brown hair with substantial blonde highlights. In these instances, the system may select the dominant color first for an assessment of the color. However in other examples, the classified colors may include blended color mixes and the selection of an avatar hair color may be based on both dominant and highlight colors. 
     It should be noted that certain user colors may be compared to naturally occurring colors, as well as optionally artificial colors. For example, some users may have pink or purple hair from hair dye and certain artificial colors may be captured with the analysis. The spectrum of identified colors may be varied based on the avatar paradigm, human fashion trends, or the like. As such, the discussion of any particular number of color combinations or detectable hues should be understood to be variable as desired or needed. 
     When the color features of the user are classified, the method  200  may proceed to operation  208  and the processor  120  selects avatar feature colors for the user&#39;s avatar  108 . The feature colors may be determined by comparing the user&#39;s classified colors to an avatar database stored in memory  126  that includes colors that fit within the selected paradigm for the avatar  108 . In other words, the selected avatar feature colors may not exactly match the user, but match a corresponding avatar color for the desired application or avatar type. For instance, some avatars may not have human skin tones, but may have a range of skin colors that can be translated to human skin tones, and so if the user&#39;s skin tone is classified as very light human skin tone, a very light avatar skin tone may be selected. The selected avatar feature colors may be stored in the memory  126  after selection and the method  200  may proceed to operation  218  where the avatar  108  is built and the color selections applied. 
     With reference again to  FIG. 3 , while the color features are being determined, the method  200  may also detect select feature characteristics. In particular, in operation  210 , the processor  120  extracts certain feature shapes and other non-color characteristics from the user images  302 . For example, the landmark masks  320 ,  322  generated in operation  204  are used to extract feature characteristics, such as the shape or boundary, of the selected images. 
     Once the user features have been extracted, the method  200  proceeds to operation  212  and the features are classified by the processor  120 . In particular, the feature shapes and characteristics are classified as being a category, type, and subtype of feature shape classified within the avatar database stored in memory  126 . Similar to the color classification, the shape classification may be done via a best fit analysis. As an example,  FIG. 7  illustrates a classification tree  400  for a user facial hair feature  402 . The facial hair  402  on a user may be classified as having a shape matching a category  404 ,  406 ,  408 ,  410  such as stubble  404 , beard  406 , mustache  408 , or no beard  410 , and under each broad category  404 ,  406 ,  408 ,  410  may be further classification options or types, such the category beards  406  may include types of chin strap  414 , goatee  416 , soul patch  418 , brett  420 , circle  422 , full, balboo  424 , and/or anchor  428 , each of which may be determined by detecting the shape of facial hair and/or position on a user&#39;s face. 
     In addition to the category and type, each type may include a further subtype such as length  432  (e.g., short, medium, or long) and/or mustache presence  434  (e.g., mustache, no mustache). The subtypes are variable, but often for hair may be based on a hair length such as short, medium, or long, where short corresponds to a short hair length having length between A to B inches, medium corresponds to C to D inches, and long corresponds to length over D, where the specific inches intervals depend on the paradigm or other use desires. In operation  212 , the processor  120  may do a best fit type of analysis using the extracted shape of the user features, as well as image information that may detect characteristics (e.g., hair length or texture). For example, a perimeter outline of the hair locations may be compared to the categories and types to determine the specific “bucket” that the user&#39;s hair shape most likely matches. Additionally, in instances where hair texture is detected, this may be done by analyzing a perimeter curve shape corresponding to the user&#39;s head shape. For example, if the top perimeter outline is a noisy curve as compared to a flatter curve, the user may be determined to have curvy hair. The more noise in the curve the tighter the hair curls. In other examples, can use different AI filters or models to determine the curliness of a user&#39;s hairline. 
     It should be noted that the categories, types, and subtypes may be varied as desired, detected user features, as well as varying fashion trends (e.g., facial hair styles captured as selected types may be updated to account for variations in fashion or the like). Subtypes may include lengths, as well as texture options, such as curly, straight, wiry, thin, thick, or the like, as well as density and mass determinations. 
     In one example, there may be 13 nose shapes of 3 different lengths and sizes, three different jaws, 0-100% for eye depth (e.g., scalable), and 7 different head shapes (e.g., round, pear, square, and so on). However, other category types and shape types may be used and depend on the desired aesthetic context or avatar paradigm. 
     Once the user&#39;s particular features are classified as belonging to a selected user grouping of features, the method  200  proceeds to operation  214  and the processor  120  selects an avatar feature characteristic corresponding to the user&#39;s classified feature. For example, if the user&#39;s facial hair is classified as being a short length chin strap with no mustache, the processor  120  may compare the category, type, and subtype determinations to corresponding avatar features, which may be determined by referencing an avatar feature database or lookup table stored in memory  126 . The avatar feature database includes similar categories and types as the categories and types for the user identification. Using the avatar feature database, the processor selects an avatar feature that matches the category, type, and subtype(s) of the user feature. It should be noted that in some instances, multiple user category, types, or subtypes may be grouped into one or more avatar category, types, and subtypes (e.g., the avatar paradigm may not include all selected categories, types, or subtypes and user classifications falling within those categories, types, or subtypes may be matched with an avatar feature having a different category, type, or subtype). After the avatar feature characteristics are selected, the method  200  proceeds to operation  218  and the avatar  108  is built with the selected characteristics and feature colors. 
     With reference again to  FIG. 3 , after the landmarks are determined in operation  204 , the method  200  may also proceed to operation  216  and the 3D avatar shapes may be determined. An example of operation  216  is shown in  FIG. 4 . As shown in  FIG. 4 , operation  216  may begin with step  230  and the processor  120  receives user 3D information from the user capturing device  104 . The user 3D information, may include depth information corresponding to user facial or other features, e.g., eyes, eyebrows, nose, and the like. For example,  FIG. 8A  illustrates a user depth capture  460  include geometric information corresponding to selected user features  462 . 
     After the user information  462  from the captured user information  460  is received, operation  204  proceeds to step  232  and the processor  120  removes obstructions, if present, from the user information captured in operation  202 . For example, the landmarks are used to determine if the user includes obstructions, such as bangs, facial hair, or the like, that may interfere with a 3D mesh of the user. Obstructions may be determined by using the eyes and mouth as landmarks and anything other than skin color detected above the eyes or around the mouth may be disregarded. In one example, the masks  320 ,  322  generated in operation  210  may be used to identify the location and shape of certain obstructions to allow their removable. For example, a Boolean operation can be used to determine if there is 3D data that falls inside the mask or outside the mask. Information falling within the mask may be disregarded since it may be assumed to be incorrect, e.g., bangs on a user&#39;s forehead may be detected as raised 3D protrusions on a user&#39;s forehead and since these protrusions fall within the facial hair mask (which may be detected via color, AI, or the like) the protrusions are disregarded and not incorporated into the topographical 3D mesh of the user&#39;s face. In this example, a border is selected around the feature mask, such as 1/12-¼ of an inch that is analyzed both above and below the location of the mask and a color determine may be used to determine if the border area is skin or hair and if hair the information is not incorporated into the mesh. The border area and/or operations used to detect the obstructions may be varied as desired. 
       FIGS. 8B and 8C  illustrate user images with obstructions, facial hair and bangs, respectively, as compared to a first 3D shape generated without the obstructions removed and a second 3D shape generated with the obstructions removed. In some embodiments, to “remove” the obstructions, certain characteristics of the user&#39;s facial structure (e.g., jawbone shape and length or forehead shape and length) may need to be estimated. In these embodiments, the other user facial information, can be used to estimate the omitted information. 
     After operation  232 , operation  216  proceeds to step  234  and a user 3D mesh is generated. For example, the processor  120  uses the landmark information and depth information detected in the user 3D information to generate a user mesh, e.g., a 3D geometric representation or point cloud corresponding to the user&#39;s features.  FIGS. 8B and 8C  illustrate examples of the initial user 3D mesh  464 ,  466 . 
     Once the user&#39;s initial mesh is generated, operation  216  proceeds to step  236  and the 3D locations are identified on the 3D mesh.  FIG. 9A  illustrates an example of a user mesh  468  with the 3D landmarks identified. As shown in  FIG. 9A , the landmarks may be overlaid on the user mesh  468 . In one example, the location of the landmarks may be determined by image analysis and then using photogrammetry techniques, the locations of the landmarks identified and overlaid on the locations in the 3D mesh. Additionally, the 3D landmarks of the user are then identified on a generic mesh. In other words, the landmarks of the user that were captured during the user scan or capture operation (e.g., eyes, nose, mouth), are identified in the generic mesh, with the generic mesh including the same number of landmarks that were identified in the user mesh. In one example, when one or more 2D user images are captured and used to generate the 3D shape, a lookup table or other point translation between the 2D and 3D locations may be performed to correlate a particular 2D feature location to a 3D location on the mesh. 
     With the locations of the 3D landmarks identified on the user mesh, operation  216  proceeds to operation  238  and the user mesh  468  is wrapped with a generic mesh. For example, the landmarks are used to align the generic mesh and the user mesh  468  and the generic mesh is wrapped over the user mesh  468 . The generic mesh includes a full measureable geometric of a standard user face and the landmarks are aligned to ensure that the generic mesh is wrapped properly around the user mesh  468 .  FIG. 9B  illustrates the wrapped mesh  470  with the landmarks  472  being aligned with those identified on the user mesh  468 . In some embodiments, there may be generic meshes for adult and children faces and optionally by gender as well (e.g., generic male face mesh and generic female face mesh). In one example, barycentric coordinates are determined for the user mesh  468 , as well as the generic mesh. Then 3D landmarks from the user are corresponded to similar landmarks on the generic mesh. From there, an iterative closest point solving algorithm may be executed by the processor to output a constant topology for the user. 
     The wrapped mesh  470  then includes a measurable geometry and the measurements for landmark or anchors dimensions can be more easily and accurately determined. For example, comparing  FIGS. 9A and 9B , the landmark locations in the user mesh  468  are clustered and difficult to analyze, but once wrapped with the generic mesh, the wrapped mesh  470  includes the landmarks spaced at the right locations and the mesh is oriented in the correct direction. In one example, the wrapping step acts to create a duplicate of the scanned user mesh, but with clean geometry that can be measured and extracted. 
     After step  238 , operation  216  proceeds to step  240  and the user&#39;s features and landmarks are measured on the wrapped mesh  470 . For example, the processor  120  uses the vertices of landmarks to determine measurements for height, width, and other measurable characteristics (e.g., perimeter boundary). In one example, the measurements may be determined based on vertices in the mesh, e.g., a nose is determined to span between vertex  352  and vertex  365 . Other measurement techniques may be used as well, e.g., pixels, inches, centimeters, etc. 
     With the user features measured, operation  216  proceeds to step  242  and the user&#39;s features are classified. As described in operation  212 , the user features may be classified based on a “bucket” or quantized approach. In particular, the normalized measurements from the wrapped mesh  470  can be compared to avatar measurement groups, e.g., small nose is anything smaller than X number of vertices, medium nose ranges from Y to Z number of vertices, and large nose is everything larger than Z vertices. It should be noted that the category ranges for the different features may vary based on the feature type, as well as the selected avatar or user characteristics (e.g., adult male, adult female, child, etc.).  FIG. 10  illustrates an example of measurement of face shapes, hair components, eyebrows, and glasses using the vertices of the wrapped mesh  470 . 
     After the user features are classified, operation  216  proceeds to step  244  and the avatar features are selected. In particular , similar to operation  214 , the processor  120  uses the classification buckets to translate the user feature into an avatar feature having the same category, type, and subcategories. A classification tree or other type of translation table may be used to correspond a user classified feature into an artistic representation of the user feature, e.g., a large avatar nose, medium avatar nose, and/or small avatar nose. A classification tree can also be used for head shape as well, e.g., whether the user&#39;s head is diamond, square, heart, triangular, round, or oval shaped, or which shape best describes the user&#39;s head shape, and this may be used to build the initial avatar. The classification process may include a best analysis that matches the user&#39;s shape to a predetermined category and/or type. 
     The operation  216  may then proceed to operation  246  and an initial avatar shape is generated by the processor  120 . For example, the processor  120  uses the classified shapes to generate an initial avatar  480  (see  FIG. 11 ) by selecting the feature shapes based on the classified elements, where the feature shapes correspond to a design paradigm for the selected avatar, rather than a pure representation of the user. In one embodiment, a head shape, size, nose shape, mouth shape, and other locational information, is used to select avatar features that are then combined together to form the initial avatar  480 . Additionally, the avatar initial shape  480  is combined with additional guest individualized features, such as wrinkles, eye shape, ear shape, eye lash length, etc. In one embodiment a wrinkle factor is applied to the initial avatar  480 , where the wrinkle factor is determined based on an age of the user, which may be input or detected (e.g., by image analysis identifying wrinkles or other age identification characteristics) and be a range or value (e.g., 0-100%), illustrated in  FIG. 10  as extreme wrinkle avatar  486  and user wrinkle avatar  488 . As another example, an eye factor, which may correspond to an eyeball or eye socket height or size, may be determined by a similar factor. For example, if the user falls within an eye shape corresponding to a 10% of users, than an eye factor of 0.1 may be applied to the initial avatar shape  480  to adjust for the user&#39;s specifics. As shown in  FIG. 10 , an eye shape end point (e.g., 100% factor) is reduced by the user&#39;s specifics to generate an eye factor for the initial avatar  480 . 
     Once the avatar is generated and select factor adjustments are complete, operation  216  proceeds to operation  248  and the generic mesh  482  is subtracted from the wrapped user mesh  470 . For example, the processor  120  extracts differences between vertices on the wrapped user mesh  470  and vertices on the generic mesh  470  to remove any incidental impact on the avatar shape imported by the generic mesh (see e.g.,  FIG. 10 ). In this manner, the user mesh may be a unique output to the user that does not include the facial shapes import by the 3D mesh. 
     In one example, the extraction is used to determine a delta between feature locations on the generic mesh as compared to the user mesh, such that the system can utilize and store the deltas, rather than a full user mesh, when generating the avatar. For example, the 3D user mesh can be analyzed to determine a vector between a selected point (e.g., end of the nose) between the user wrapped mesh and the generic mesh. The vector may then be stored, rather than the actual user mesh information, and used to recreate the user mesh or build the avatar, since the system will apply the delta to the generic mesh to arrive at the user mesh, rather than storing the original user mesh. In these embodiments, security may be enhanced for the system, as certain processing, such as the building of the avatar, that may be done remotely or on a cloud server, may not need to receive the user specific mesh and data, only deltas information. This reduces the transfer of personal information for the user between devices. Further, by sending the delta information, rather than a full 3D mesh, the data transfer between the cloud and the computer or between computing devices, is reduced, increasing speed and efficiency. In this instance, once the delta or distances between the generic mesh and the user mesh are determined, the user mesh can be discarded or destroyed, since the system may not need to further reference the full user mesh. 
     Operation  216  then proceeds to step  350  and the avatar 3D shape  484  is output. The avatar 3D shape  484  includes the selected features falling within the avatar design paradigm, but that are based on the distinct user features detected and measured. 
     With reference again to  FIG. 3 , once the 3D avatar shape  484  is generated, the colors and feature characteristics determined in operations  208  and  214 , respectively, are combined in operation  218  when the processor  120  builds the avatar. For example, the processor  120  combines the selected facial features (e.g., facial hair, hairline, accessories, or the like), with the avatar colors (e.g., hair color, eye color, skin color), onto the avatar 3D facial shape to generate the actual avatar icon  492  (see  FIG. 12 ). The avatar  492  includes the structural features, illustrated features, and colors that correspond to the user, yet remain within the confines of the design language selected for the avatar. This allows avatars to be generated that appear to be similar to a user, but still fit within the design framework, e.g., allow a user to experience how a character inspired by the them may be represented within an animated movie or the like. In this manner, changes in the design paradigm may result in dramatically different appearances for the avatar, but still having an individualized aesthetic corresponding to the user. Furthermore, in some instances, the personalized avatar, which may be identifiable as the user, can be stored in the system and other user specific information (e.g., 2D images, 3D mesh, etc.) can be discarded. This may allow the system to provide a user engagement vehicle (the avatar) that is identifiable as the user, but does not contain user specific information, since the selected features are from the paradigm of avatar features, but assembled in a manner that is similar to the user. Using this representation, the actual personal information for the user can be destroyed, while still allowing the user to full engage with the system in a meaningful and individualized manner. 
     Conclusion 
     The methods and systems are described herein with reference to avatars generated based on user&#39;s faces. However, these techniques are equally applicable to other types of representative electronic representations of a user or character (e.g., animals, full human bodies, or the like). Additionally, although the discussions presented herein are discussed with respect to symmetrical features (e.g., eyes, ears, eyebrows), in some instances, each landmark may be identified separately, allowing the system to more accurately account for asymmetry between features, e.g., different eye heights, different eyebrow shapes, or the like. 
     In methodologies directly or indirectly set forth herein, various steps and operations are described in one possible order of operation but those skilled in the art will recognize the steps and operation may be rearranged, replaced or eliminated without necessarily departing from the spirit and scope of the present invention. It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the spirit of the invention as defined in the appended claims.