Patent Publication Number: US-11662829-B2

Title: Modification of three-dimensional garments using gestures

Description:
RELATED APPLICATION 
     This application is a continuation of and claims priority to U.S. patent application Ser. No. 16/392,093 filed Apr. 23, 2019, which is a continuation of and claims priority to U.S. patent application Ser. No. 14/675,241 filed Mar. 31, 2015. The disclosures of these applications are incorporated by reference herein in their entirety. 
    
    
     TECHNICAL FIELD 
     The present application relates generally to the technical field of data processing, specifically, three-dimensional (3D) modeling and simulation. 
     BACKGROUND 
     Shopping for clothes in physical stores can be an arduous task and, due to travelling and parking, can be very time consuming. With the advent of online shopping, consumers can purchase clothing, while staying home, via a computer or any other electronic device connected to the Internet. Additionally, purchasing clothes online can be different in comparison with purchasing clothes in a store. One difference is the lack of a physical dressing room to determine if and how an article of clothing fits the particular consumer. Since different consumers can have different dimensions, seeing how an article of clothing fits, by use of a dressing room, can be a very important aspect of a successful and satisfying shopping experience. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic diagram illustrating an example system for generating and modifying a 3D garment model, in accordance with certain example embodiments. 
         FIG.  2    is a flow diagram of a process for modifying a garment based on a gesture, in accordance with certain example embodiments. 
         FIG.  3    is a flow diagram of a process for transmitting a modification request based on a confirmation gesture, in accordance with certain example embodiments. 
         FIG.  4    is a flow diagram of a process for draping a 3D garment model on an avatar, in accordance with certain example embodiments. 
         FIG.  5    is a flow diagram of a process for determining a modification gesture based on sensor data, in accordance with certain example embodiments. 
         FIG.  6    illustrates a method of facilitating the online purchase of garments, in accordance with example embodiments. 
         FIG.  7    illustrates a method of facilitating the online purchase of garments, in accordance with example embodiments. 
         FIG.  8    is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium and perform any one or more of the methodologies discussed herein. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Example systems and methods are directed to modifying (e.g., tailoring) garments in a virtual fitting room based a gesture from a user. Provision of a virtual fitting room can include recognition of a gesture representing a command to initiate an action on behalf of a user. Examples merely illustrate possible variations. Unless explicitly stated otherwise, components and functions are optional and can be combined or subdivided, and operations can vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter can be practiced without these specific details. 
     According to some embodiments, a system can display (e.g., on an augmented reality headset, virtual reality headset, television screen, computer screen, or mobile device screen) a representation of a garment (hereinafter “three-dimensional (3D) garment model”) draped on a representation of a user (hereinafter “avatar”). The 3D garment draped (e.g., mapped) on the avatar can allow the user to virtually try on a garment available for sale in a virtual dressing room. The garment can also include other items that are related to clothing, such as footwear, purses, jewelry, or accessories. 
     One example of such action is a system that allows a user to purchase garments using an electronic marketplace. For example, the user finds a garment in the electronic marketplace and adds the garment in a virtual dressing room. Then, using sensors, the system can generate an avatar corresponding to the user. Additionally, a 3D garment model corresponding to the garment is accessed and draped on the avatar. The 3D garment model draped on a mirrored self of the user (e.g., avatar) can be presented to the augmented reality or virtual reality goggles worn by the user. The user can adjust the size of the garment using a modification gesture. The modification gesture is captured by the sensors and determined by the system. For example, the user can try a larger size of the garment by using a pinching and pulling gesture. Subsequently, the user can accept the larger-sized garment using a confirmation gesture. Alternatively, the garment can be custom-tailored (e.g., the sleeves shortened) by the user using modification gestures. 
     In some instances, the 3D garment model is representative of real clothing available for purchase using an electronic marketplace. For example, the user can virtually try on the garment available for sale while the user is in his home (e.g., using a virtual reality headset), or in front of a store (e.g., using a virtual store front). 
     In various example embodiments, the system can generate an avatar based on a first and second set of sensor data accessed from a first sensor and second sensor. In some instances, the avatar can be further generated based on a third set of sensor data accessed from a third sensor. Additionally, the system can drape the 3D garment model on the avatar. The system can also determine a gesture performed by the user, and that the gesture represents a command to initiate an action to modify the 3D garment model. Examples of such actions that correspond to user gestures include a pinching and pulling gesture, a pinching and tucking gesture, a hand stretching gesture, a hand pinching gesture, a hand nipping gesture, and so on. 
     By way of examples, the pinching gesture can be the finger motion of bringing two fingers together. The stretching gesture can be the finger motion of bringing two fingers apart. The pulling gesture can be the hand motion of pulling a garment section to elongate the garment section. For example, the pinching and pulling gesture is the combination of the pinching gesture and the pulling gesture, which can be bringing the two fingers together, then pulling a section of the garment using a hand motion to elongate it. The tucking gesture can include tucking in one or more fingers, or tucking the whole hand inside a part of the garment so that the hand is not visible. The hand nipping gesture includes using a hand to squeeze a part of the body. In some instances, one or more of these gestures can be combined using one or both hands. 
     In some example embodiments, the system can prompt the user to confirm that the gesture represents the command (e.g., the user intended to issue the command when making the gesture). Based on the user confirming that the gesture represents the command, the system can modify (e.g., tailor) the 3D garment on behalf of the user. Examples of a confirmation gesture include repeating the gesture, making a general gesture associated with confirmation (e.g., an “okay” gesture by connecting the thumb and forefinger in a circle and holding the other fingers straight), or issuing a voice command. 
     In various example embodiments, the user can employ a particular gesture to change the color, pattern, or texture of a representation of the garment the user is virtually trying on. In various example embodiments, the 3D garment model can include garments that the user is currently wearing. In certain example embodiments, the 3D garment model can include garments that are being offered for sale by a merchant. 
     In some example embodiments, the system can receive sensor data descriptive of the body of the user in a 3D physical space. The sensor data can be received from a sensor (e.g., depth sensor). The system can generate an avatar of the user based on the sensor data descriptive of the body of the user. The avatar can also include a first shape based on the sensor data received at a first time and a second shape based on the sensor data received at a second time. Then, the determining of the gesture can be performed by analyzing a difference between the first and second shapes. The system can also determine and perform an action that corresponds to the gesture. 
     In some example embodiments, the system, using a depth sensor, determines the user&#39;s body measurements based on positional data points determined by the system based on the sensor data captured by the depth sensor. The system can pre-filter the garments whose representations should be presented to the user such that only representations of garments matching the user&#39;s body measurements can be presented to the user. For example, if the system determines that the user wears a medium-sized shirt based on the measurements of the user&#39;s body, then the system presents medium-sized shirts to the user. 
     In various example embodiments, the system can use cloth physics technology to drape a 3D garment model on an avatar based on a material property of the garment. The material property can reflect the features of the fabric from which the garment was made. For example, garments made from different fabrics can hang or move differently based on the type of fabric used to manufacture the particular garment. Thus, using cloth physics technology in draping 3D garments on the avatar allows a user to see how the real physical item of clothing would move when worn by the user. 
     In some example embodiments, the sensor data can be used to generate (e.g., represent, model, or define) a 3D field of view that can be displayed on a screen (e.g., of an augmented reality headset, virtual reality headset, television, computer, or mobile device). Examples of such sensor data include the locations and shapes of objects in relation to the location of the sensor. In various example embodiments, based on the received sensor data, the system can determine details of the objects in the room, such as spatial measurements of the objects in the room (e.g., of the user&#39;s body or of the furniture in the room). In some example embodiments, based on the received sensor data, the system can determine gestures made by the user. Some devices (e.g., that can include a depth sensor and a camera) can detect other details of the objects in the room (e.g., texture, color, or pattern of the clothing worn by the user or of a wall of the room). 
     Reference will now be made in detail to various example embodiments, some of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure and the described embodiments. However, the present disclosure can be practiced without these specific details. 
       FIG.  1    is a schematic diagram illustrating an example system for generating and modifying a 3D garment model, in accordance with certain example embodiments. The network environment  100  includes memory  110 , a database  115 , an electronic marketplace  120 , and devices  130  and  140 , all communicatively coupled to each other through a network  150 . 
     In some example embodiments, the memory  110 , or a computer-readable storage medium of the memory  110 , stores the following programs, modules, and data structures, or a subset thereof: a garment simulation module  111 , an access module  112 , and a display module  113 . 
     In some example embodiments, the database  115  can include an asset library to store 2D or 3D representations of body types and body measurements associated with a user, as well as 2D or 3D representations of clothing items (e.g., garments or other objects). 
     As shown in  FIG.  1   , the memory  110 , the database  115 , the electronic marketplace  120 , some, or all of them, can form all or part of a network-based system  105 . The network-based system  105  can include one or more processing units (CPUs) for executing software modules, programs, or instructions stored in the memory  110  and thereby performing processing operations; one or more communications interfaces; and one or more communication buses for interconnecting these components. The communication buses can include circuitry (e.g., a chipset) that interconnects and controls communications between system components. The network-based system  105  also optionally includes a power source and a controller coupled to the database  115 . The network-based system  105  optionally includes a user interface comprising a display device and a keyboard. 
     Also shown in  FIG.  1    are users  132  and  142 . One or both of the users  132  and  142  can be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the device  130  or device  140 ), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user  132  is not part of the network environment  100 , but is associated with the device  130  and can be the user of the device  130 . For example, the device  130  can be an augmented reality headset, a virtual reality headset, a desktop computer, a vehicle computer, a tablet computer, a navigational device, a portable media device, or a smartphone belonging to the user  132 . Likewise, the user  142  is not part of the network environment  100 , but is associated with the device  140 . As an example, the device  140  can be an augmented reality headset, a virtual reality headset, a desktop computer, a vehicle computer, a tablet computer, a navigational device, a portable media device, or a smartphone belonging to the user  142 . An example of an augmented reality headset is the Microsoft HoloLens® headset. An example of a virtual reality headset is the Oculus Rift  0  headset. 
     Also shown in  FIG.  1    are sensors  134 ,  136 , and  138  (e.g., a Kinect™ device, a depth sensor, a smartphone, or a camera). In some instances, the sensor  134  includes a depth sensor, a red-green-blue (RGB) camera, and a microphone. The system (e.g., the network environment  100 ) can include one or more sensors. In some example embodiments, one or more of the sensors  134 ,  136 , or  138  can be part of the device  130 . In other example embodiments, the sensors  134 ,  136 , or  138  can be external to the device  130 . Each sensor  134 ,  136 , and  138  can capture (e.g., receive, gather, or collect) sensor data (e.g., spatial data) about the physical space external to the sensor (e.g., spatial data about the user  132 ) and transmit the captured sensor data to the device  130 , which in turn can transmit some or all of the sensor data captured by the sensor  134 ,  136 , or  138  to the garment simulation module  111  via the network  150 . In some example embodiments, the sensor  134 ,  136 , or  138  can communicate with and send the captured sensor data to the garment simulation module  111  via the network  150  without first sending the sensor data to the device  130 . 
     The network  150  can be any network that enables communication between or among machines, databases, and devices (e.g., the garment simulation module  111  and the device  130 ). Accordingly, the network  150  can be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network  150  can include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof. Accordingly, the network  150  can include one or more portions that incorporate a local area network (LAN), a wide area network (WAN), the Internet, a mobile telephone network (e.g., a cellular network), a wired telephone network (e.g., a plain old telephone system (POTS) network), a wireless data network (e.g., a Wi-Fi network or a WiMAX network), or any suitable combination thereof. Any one or more portions of the network  150  can communicate information via a transmission medium. As used herein, “transmission medium” refers to any intangible (e.g., transitory) medium that is capable of communicating (e.g., transmitting) instructions for execution by a machine (e.g., by one or more processors of such a machine), and includes digital or analog communication signals or other intangible media to facilitate communication of such software. 
     The memory  110  can include high-speed random-access memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), double data rate random-access memory (DDR RAM), or other random-access solid state memory devices. Additionally, the memory  110  can include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. The memory  110  can optionally include one or more storage devices remotely located from the CPU. The memory  110 , or alternately the non-volatile memory device within the memory  110 , can be or include a non-transitory computer-readable storage medium. 
     The garment simulation module  111  can generate an avatar (e.g., 3D body model) based on the accessed sensor data from the sensors  134 ,  136 , or  138 . In some instances, the garment simulation module  111  can position the avatar inside a 3D garment model of a garment available for sale. Moreover, the garment simulation module  111  can calculate simulated forces acting on the 3D garment model based on the positioning of the avatar inside the 3D garment model and the material property of the garment. The garment simulation module  111  can generate an image of the 3D garment model draped on the avatar based on the sensor data or the calculated simulated forces. The simulated forces can be calculated, for example, by the garment simulation module  111 , based on 3D garment tessellation techniques. 
     The access module  112  can communicate with devices (e.g., the device  130  or the device  140 ) via the one or more communications interfaces (e.g., wired or wireless), the network  150 , other wide area networks, local area networks, metropolitan area networks, and so on. Additionally, the access module  112  can access information for the memory  110  via a communication bus. The access module  112  can access information stored in the database  115 . Additionally, when the 3D garment models or avatar is stored in the device  130 , the access module  112  can access the user&#39;s information in the device  130  via the network  150 . Alternatively, when the 3D garment models or avatar is stored on a cloud server, the access module  112  can access the user&#39;s information in the cloud server via the network  150 . 
     The display module  113  is configured to cause presentation of the generated image on a display of a device (e.g., device  130 ). For example, the display module  113  can present a 3D image or simulation on the display of virtual reality goggles. The 3D simulation can be based on the actions of the garment simulation module  111  and the access module  112 . 
     Any of the machines, databases, or devices shown in  FIG.  1    can be implemented in a general-purpose computer modified (e.g., configured or programmed) by software to be a special-purpose computer to perform the functions described herein for that machine, database, or device. For example, the garment simulation module  111 , the access module  112 , the display module  113 , the database  115 , the electronic marketplace  120 , and the devices  130  and  140  can be each be implemented in a computer system, in whole or in part, as described below with respect to  FIG.  8   . As used herein, a “database” is a data storage resource and can store data structured as a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. Moreover, any two or more of the machines, databases, or devices illustrated in  FIG.  1    can be combined into a single machine, and the functions described herein for any single machine, database, or device can be subdivided among multiple machines, databases, or devices. 
       FIG.  2    is a flowchart representing a method  200  for modifying a 3D garment based on gestures of a user, according to example embodiments. The method  200  is governed by instructions stored in a computer-readable storage medium and that are executed by one or more processors of the network-based system  105 . Each of the operations shown in  FIG.  2    can correspond to instructions stored in a computer memory (e.g., memory  110 ) or computer-readable storage medium. 
     Operations in the method  200  can be performed by the garment simulation module  111 , the access module  112 , or the display module  113 . As shown in  FIG.  2   , the method  200  includes operations  210 ,  220 ,  230 ,  240 ,  250 ,  260 , and  270 . The garment simulation module  111  can configure a processor among the network-based system  105  to perform the operations of the method  200 . 
     At operation  210 , the access module  112  accesses a first set of sensor data from a first sensor. The first sensor is located at a first location, such as in front of the user. The first sensor can be a camera, a depth sensor, a heat sensor, a radar sensor, an acoustic sensor, and so on. The first set of sensor data can include spatial data received from the first sensor (e.g., sensor  134 ). The access module  112  accesses (e.g., receives) the sensor data obtained from the sensor  134 . The sensor data can include spatial data about the physical space external to the sensor. In some instances, the sensor data is transmitted to the device  130 , which in turn can transmit some or all of the sensor data to the network-based system  105  via the network  150 . In some other instances, the sensor  134  can communicate with and send the captured sensor data to the network-based system  105  via the network  150  without first sending the sensor data to the device  130 . 
     In some instances, the first set of sensor data includes 2D range images. A 2D range image can have pixel values corresponding to distance information for an object in relation to the first sensor, which can be located in front of the user. For example, pixel values corresponding to a blue color variation can be associated with an object farther away from the first sensor, while pixel values corresponding to red color variation can be associated with an object that is closer to the first sensor. 
     At operation  220 , the access module  112  accesses a second set of sensor data from a second sensor. The second sensor can be a camera, a depth sensor, a heat sensor, a radar sensor, an acoustic sensor, and so on. The second sensor is located at a second location which is different than the first location. For example, the second location can be behind the user. The access module  112  accesses (e.g., receives) the sensor data obtained from the second sensor (e.g., sensor  136 ). In some instances, the second set of sensor data is transmitted to the device  130 , which in turn can transmit some or all of the sensor data to the network-based system  105  via the network  150 . In some other instances, the sensor  136  can communicate with and send the second set of sensor data to the network-based system  105  via the network  150  without first sending the sensor data to the device  130 . 
     In some instances, the second set of sensor data includes 2D range images having pixel values corresponding to distance information for an object in relation to the second sensor. The second sensor can be located behind the user. Alternatively, when three sensors are used, the sensors can be located in a triangular perimeter around the user. An implementation with three sensors can include a first sensor located in front of the user (e.g., 12 o&#39;clock position of a 12-hour clock), a sensor located behind and to the right of the user (e.g., 4 o&#39;clock position of a 12-hour clock), and the third sensor located behind and the left of the user (e.g., 8 o&#39;clock position of a 12-hour clock). 
     In some instances, the accessing of the first set of sensor data from the first sensor is performed by a wireless transceiver. 
     At operation  230 , the garment simulation module  111  generates a 3D garment model of a garment available for sale draped on an avatar. The 3D garment model and the avatar are generated based on the first set of sensor data and the second set of sensor data.  FIG.  4    further describes techniques for generating the avatar and the 3D garment model based on the first and second sets of sensor data. 
     In some instances, the avatar can be generated by stitching a 2D representation of the front of a body profile together with a 2D representation of the back of the body profile. Then the 3D garment model is draped on the avatar based on calculated simulated forces. 
     Additionally, the 3D garment model can be a tessellated 3D garment model. The tessellated 3D garment model can include a group of vertices associated with points on the surface of the garment. The garment points can be generated using a tessellation technique. For example, a shirt can be tessellated with triangles (e.g., about 20,000 triangles when a triangle edge is around 1 centimeter), and the vertices of the triangles can be the garment points of the 3D garment model. The garment points can include location information such as an x, y, and z position value. The simulated forces which are discussed in  FIG.  4    (e.g., at operation  440 ) can be calculated for each garment point. U.S. Non-Provisional application Ser. No. 14/270,244 filed on May 5, 2014, which is incorporated herein by reference, describes techniques for generating a tessellated 3-D garment model. 
     The garment simulation module  111  can position at least a portion of the avatar inside the garment points. In some instances, positioning can include placing the garment model on or around the avatar. In these instances, the garment model can be stretched and deformed based on the simulation. As previously mentioned, the garment model can consist of a set of shapes (e.g., triangles) to form the surface of the garment model. The shapes can be created using lines connecting the vertices. Additionally, the garment model can include physical properties associated with the lines (e.g., edges) and vertices in the tessellated 3D garment model. 
     The garment simulation module  111  can simulate the garment model on the generated user avatar. In some instances, simulation of the garment can include placing the garment around the avatar at an appropriate position, and running simulations. The simulation can advance the position and other related variables of the vertices of the garment model based on different criteria (e.g., the laws of physics, garment material properties, body-garment interaction). The result is a large system of equations (e.g., one variable for each force component) that the garment simulation module  111  can solve in an iterative fashion. The simulation can be completed when the simulation becomes stable. For example, the simulation can become stable when the garment model reaches a steady state with a net force of zero. The U.S. Non-Provisional application Ser. No. 14/270,244 filed on May 5, 2014, which has been previously incorporated herein by reference, describes techniques for the garment simulation module  111  to simulate the garment model on the generated user avatar using a large system of equations. 
     At operation  240 , the display module  113  causes a presentation, on a display of a device, of the 3D garment model draped on the avatar. In some instances, the device can be a virtual reality headset. Additionally, the device can be the device  130  or the device  140 . Furthermore, the avatar can correspond to a body model of the user  132 , and the 3D garment draped on the avatar can be presented to either the user  132  or the user  142 . 
     In addition to presenting the 3D garment model draped on the avatar, the sensor data can be used to generate a 3D field of view that can be displayed on the display of the device. In various example embodiments, based on the received sensor data, the system can determine details of the objects in the room, such as spatial measurements of the objects in the room (e.g., of the furniture in the room). 
     In some instances, the generating of the 3D garment model draped on the avatar is performed by a graphics processing unit. 
     At operation  250 , the garment simulation module  111  determines a modification gesture associated with the 3D garment model draped on the avatar based on the first set of sensor data and the second set of sensor data. The modification can include modifying the existing garment that is draped on the avatar, such as altering the garment based on the modification gesture. In some instances, the modification gesture is confined to the existing garment.  FIG.  5    further describes techniques for determining the modification gesture. 
     For example, the garment simulation module  111  determines that the avatar has a first shape based on the sensor data received at a first time. Additionally, the garment simulation module  111  determines a second shape for the avatar based on the sensor data received at a second time, which occurs after the first time. Then, the determining of the gesture can be performed by analyzing a difference between the first and second shapes. By analyzing the difference between the first and second shapes, the garment simulation module  111  determines the modification gesture performed by the user, and that the modification gesture represents a command to initiate an action to modify the 3D garment model. Examples of such actions that correspond to user gestures include a pinching and pulling gesture, a pinching and tucking gesture, a hand stretching gesture, a hand pinching gesture, a hand nipping gesture, and so on. By way of examples, the pinching gesture can be the finger motion of bringing two fingers together. The stretching gesture can be the finger motion of bringing two fingers apart. The pulling gesture can be the hand motion of pulling a garment section to elongate the garment section. For example, the pinching and pulling gesture is the combination of the pinching gesture and the pulling gesture, which can be bringing the two fingers together, then pulling a section of the garment using a hand motion to elongate it. The tucking gesture can include tucking in one or more fingers, or tucking the whole hand inside a part of the garment so that the hand is not visible. The hand nipping gesture includes using a hand to squeeze a part of the body. In some instances, one or more of these gestures can be combined using one or both hands. 
     At operation  260 , the garment simulation module  111  modifies the 3D garment model based on the determined modification gesture. For example, based on the modification gesture, a section of the garment model (e.g., sleeve length, leg length, waist size, neck size) can be shortened, reduced, enlarged, or lengthened. Additionally, the fit of the garment (e.g., altering a shirt) can be changed based on the modification gesture. 
     At operation  270 , the display module  113  updates the presentation, on the display of the device, of the modified 3D garment model draped on the avatar. 
     In some instances, the method  200  can further include accessing a third set of sensor data from a third sensor. The first sensor, the second sensor, and the third sensor are positioned in a triangle configuration, such as an equilateral triangle configuration. Additionally, the generating of the 3D garment model draped on the avatar at operation  230  is further based on the third set of sensor data. Furthermore, the determining of the modification gesture at operation  250  is further based on the third set of sensor data. 
     In some instances, the garment simulation module  111  can prompt the user to confirm that the gesture represents the command (e.g., the user intended to issue the command when making the gesture). Based on the user confirming that the gesture represents the command, the system can modify (e.g., tailor) the 3D garment on behalf of the user. 
       FIG.  3    is a flowchart representing a method  300  for confirming a modification of a garment (e.g., physical garment), according to example embodiments. The method  300  is governed by instructions stored in a computer-readable storage medium and that are executed by one or more processors of the network-based system  105 . Each of the operations shown in  FIG.  3    can correspond to instructions stored in a computer memory (e.g., memory  110 ) or computer-readable storage medium. Operations in the method  300  can be performed by the garment simulation module  111 , the access module  112 , or the display module  113 . As shown in  FIG.  3   , the method  300  includes operations  310  and  320 . 
     At operation  310 , the garment simulation module  111  determines a confirmation gesture for the 3D garment model modified at operation  260 . The confirmation gesture is based on the first set of sensor data and the second set of sensor data. The confirmation gesture can be determined using similar techniques to those later described by method  500 . 
     For example, continuing with the example described at operation  250 , the garment simulation module  111  can determine that the avatar has a third shape based on the sensor data received at a third time. Additionally, the garment simulation module  111  determines a fourth shape for the avatar based on the sensor data received at a fourth time, the fourth time being after the third time. 
     For example, the first shape is of the user standing still in front of the sensors. The second shape can be associated with the user performing the hand modification gesture. The third shape can once again be the user standing still in front of the sensors. The fourth shape can be associated with the user performing the hand confirmation gesture. 
     The determining of the confirmation gesture can be performed by analyzing a difference between the third and fourth shapes. By analyzing the difference between the third and fourth shapes, the garment simulation module  111  determines the confirmation gesture performed by the user. 
     Examples of a confirmation gesture include repeating the modification gesture, making a general gesture associated with confirmation (e.g., “okay” gesture by connecting the thumb and forefinger in a circle and holding the other fingers straight), or issuing a voice command. 
     At operation  320 , the garment simulation module  111  transmits a request to modify the garment available for sale based on the confirmation gesture determined at operation  310 . For example, the request is transmitted to the electronic marketplace  120  to modify (e.g., tailor) the garment based on the modification gesture received by the user. 
     In some instances, the garment simulation module  111  sends custom sizing information to a manufacturer when the manufacturer-predefined sizes do not fit the customer correctly. Subsequently, the garment simulation module  111  can request the manufacturer to open a custom order for the customer. 
       FIGS.  4  and  5    further describe techniques initially described in  FIG.  2   , according to example embodiments.  FIG.  4    is a flowchart further describing operation  230  of  FIG.  2   .  FIG.  5    is a flowchart further describing operation  250  of  FIG.  2   . 
       FIG.  4    is a flowchart describing a method  400  for generating the 3D garment draped on the avatar based on the first and second sets of sensor data, according to example embodiments. The method  400  is governed by instructions stored in a computer-readable storage medium and that are executed by one or more processors of the network-based system  105 . Each of the operations shown in  FIG.  4    can correspond to instructions stored in a computer memory (e.g., memory  110 ) or computer-readable storage medium. Operations in the method  400  can be performed by the garment simulation module  111 , the access module  112 , or the display module  113 . As shown in  FIG.  4   , the method  400  includes operations  410 ,  420 ,  430 ,  440  and  450 . 
     At operation  410 , the garment simulation module  111  generates an avatar based on the first set of sensor data and the second set of sensor data. As previously mentioned, the first set of sensor data and the second set of sensor data are accessed at operations  210  and  220 . The garment simulation module  111  accesses sensor data descriptive of the body of the user (e.g., waist size, chest size, height, arm length, leg length) in a 3D physical space. The sensor data can be received from a sensor. The garment simulation module  111  generates the avatar of the user based on the sensor data descriptive of the body of the user. 
     In some instances, in order to reduce the rendering or processing time, the garment simulation module  111  can generate an avatar for the user based on a body profile. For example, based on the sensor data, data descriptive of the body of the user (e.g., waist size, chest size, height, arm length, leg length) can be measured. Then, the avatar can be generated based on the measured body parameters. Additionally, when the body profile includes a plurality of computed measurements (e.g., waistband size, high hip, low hip, thigh, knee, an inseam length, a fit, a cut), the generated avatar becomes a more accurate representation of the user. 
     At operation  420 , the garment simulation module  111  determines a size for the garment available for sale based on the generated avatar. For example, the garment simulation module  111  using the accessed sensor data can determine positional data points. Additionally, the garment simulation module  111  determines the user&#39;s body measurements based on positional data points. 
     In some instances, the garment simulation module  111  can be configured to determine a size from a set of sizes for the garment based on the simulated forces which are described later at operation  440 . For example, the garment simulation module  111  can initially recommend a size, and then calculate simulated forces based on the initial recommendation, and update the size recommendation based on the calculated simulated forces in an iterative process. Accordingly, the display module  113  can present the garment model with a recommended size to the user. Furthermore, the garment simulation module  111  can determine a recommended size based on the available garment sizes stored in the database  115  or the electronic marketplace  120 . 
     Techniques for recommending a size from the given set of sizes for a garment are provided, in accordance with example embodiments. For example, tops are usually distributed in a few generic sizes (e.g., XS, S, M, L, XL, XXL). By computing the calculated forces for each size for the user&#39;s avatar, the garment simulation module  111  can suggest a recommended size. The recommended size can be based on the size that best fits the avatar&#39;s dimensions, or the recommendation could be based on the garment fit guidance from a manufacturer, designer, or stylist. 
     At operation  430 , the garment simulation module  111  accesses the 3D garment model. The 3D garment model can be stored in a garment model database stored in an electronic marketplace. For example, the electronic marketplace can maintain a database of garments available for sale. Additionally, the 3D garment model can include metadata information, such as a material property of the garment (e.g., elasticity, stiffness, fabric of garment, weight), price information, available quantity information, size information, fitting features (e.g., based on manufacturer), and so on. In some instances, the garment simulation module  111  pre-filters the garments to be presented to the user such that only 3D garment models matching the body measurements are presented to the user. For example, if the garment simulation module  111  determines that the user wears a medium-sized shirt based on the body measurements, then the display module  113  presents medium-sized shirts draped on the avatar of the user. 
     In some instances, the metadata information for the 3D garment model can include one or more model features. A model feature, which is an example of a fitting feature, refers to characteristics that are distinctive to the specific garment. For example, when the garment is a pair of jeans, the fitting features can include a waistband, a high hip (e.g., 3″ down from top of waistband), a low hip (e.g., 6″ down from top of waistband), a thigh measurement (e.g., circumference), a knee measurement, an inseam length, a fit (e.g., slim, normal, loose), and a cut (boot cut, relaxed, skinny, taper, straight). The list of model features is just representative, and is not intended to be exhaustive. 
     At operation  440 , the garment simulation module  111  can calculate simulated forces acting on the 3D garment based on the material property of the garment. For example, the avatar generated at operation  410  is positioned inside the 3D garment model accessed at operation  430 , and the simulated forces based on the positioning can be calculated. For example, the simulated forces can be calculated for each garment point in a tessellated 3D garment model. 
     In some instances, the garment simulation module  111  can use cloth physics technology to generate a 3D representation of a garment based on the material property of the garment. The material property can reflect the features of the fabric from which the garment was made. For example, garments made from different fabrics can hang or move differently based on the type of fabric used to manufacture the particular garment. 
     In some example embodiments, the simulated force can include a gravitational force, an elastic force, a friction force, or an aerodynamic force. Additionally, the garment simulation module  111  can further calculate the simulated forces acting on a subset of the garment points based on material properties of the garment. For example, the simulated forces can include a gravitational force and an elastic force, and the material properties of the garment can indicate a degree to which the garment is elastic. The material properties of the garment can include, but are not limited to, a sheerness value, a linear stiffness value, and a bending stiffness value. 
     At operation  450 , the garment simulation module  111  drapes the 3D garment model on the avatar based on the calculated forces. The calculated forces and the cloth physics technology allows the user to see how the real physical item of clothing moves when worn by the user. In some instances, the garment simulation module  111  can generate an image of the 3D model descriptive of the garment draped on the generated avatar based on the calculated simulated forces. The garment simulation module  111  can configure at least a graphics processing unit to generate the image. The 3D model can be presented based on a simulated force. The presentation can be done by digitally draping the 3D model onto the avatar. Optionally, the display module  113  can present the generated avatar to a user. The display module  113  can present the generated image on a display of a device. The display module  113  can configure a user interface for the presentation. 
       FIG.  5    is a flowchart describing a method  500  for determining a gesture, according to example embodiments. The method  500  is governed by instructions stored in a computer-readable storage medium and that are executed by one or more processors of the network-based system  105 . Each of the operations shown in  FIG.  5    can correspond to instructions stored in a computer memory (e.g., memory  110 ) or computer-readable storage medium. Operations in the method  500  can be performed by the garment simulation module  111 , the access module  112 , or the display module  113 . As shown in  FIG.  5   , the method  500  includes operations  510  and  520 . 
     As previously mentioned, the first set of sensor data and the second set of sensor data are accessed at operations  210  and  220 . The first set of sensor data includes initial sensor data received at a first time period and final sensor data received at a second time period, the second time period being after the first time period. The second set of sensor data can also include initial sensor data received at a first time period and final sensor data received at a second time period. 
     At operation  510 , the garment simulation module  111  detects a difference between the initial sensor data and the final sensor data. For example, the garment simulation module  111  generates a first model based on the initial sensor data. Additionally, the garment simulation module  111  generates a second model based on the final sensor data. Then, the garment simulation module  111  detects a difference between the first model and the second model. 
     At operation  520 , the garment simulation module  111  can determine a modification gesture based on the difference detected at operation  510 . The difference between the first and second models corresponds to a gesture performed by the user between the first time period and the second time period. The gesture represents a command to modify the garment available for sale in the electronic marketplace on behalf of the user. 
     In some instances, the modification gesture is a hand pulling gesture. Additionally, the modifying the 3D garment model at operation  260  can include elongating a section of the 3D garment model based on the hand pulling gesture. 
     In some instances, the modification gesture is a hand stretching gesture. Additionally, the modifying the 3D garment model at operation  260  can include elongating a section of the 3D garment model based on the hand stretching gesture. 
     In some instances, the modification gesture is a hand pinching gesture. Additionally, the modifying the 3D garment model at operation  260  can include shortening a section of the 3D garment model based on the hand stretching gesture. 
     In some instances, the modification gesture is a pinching and pulling gesture. Additionally, the modifying the 3D garment model at operation  260  can include converting the 3D garment model to a smaller size based on the pinching and pulling gesture. 
     In some instances, the modification gesture is a pinching and tucking gesture. Additionally, the modifying the 3D garment model at operation  260  can include converting the 3D garment model to a larger size based on the pinching and tucking gesture. 
     Additionally, the garment simulation module  111  can determine a confirmation gesture using similar techniques to those described at operations  510  and  520 . For example, the garment simulation module  111  can prompt the user to confirm that the gesture represents the command (e.g., the user intended to issue the command when making the gesture). Based on the user confirming that the gesture represents the command, the garment simulation module  111  can initiate the action within the electronic marketplace on behalf of the user. 
     Although individual operations of the methods  200 ,  300 ,  400 , and  500  are illustrated and described as separate operations, one or more of the individual operations can be performed concurrently, or omitted from the methods. Additionally, the operations can be performed in a different order. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     According to various example embodiments, one or more of the methodologies described herein can facilitate the online purchase of garments. As illustrated in  FIG.  6   , some example embodiments described herein can generate an avatar of a customer  610  based on accessed sensor data from operations  210  and  220 . Additionally, a 3D garment model of a garment for sale  620  can be accessed at operation  430 . Subsequently, the 3D garment model can be draped on the avatar  630  at operation  450 . Furthermore, the customer can modify the 3D garment using a modification gesture, and confirm the modification using a confirmation gesture. 
     Moreover, one or more of the methodologies described herein can facilitate the visualization of different styles of a garment on the avatar using the garment simulation module  111 . For example,  FIG.  7    illustrates how a customer can visualize the look and feel of different pairs of khakis. In this example, the customer can visualize that the signature khaki  710  is a looser fit, in comparison to the alpha khaki. Additionally, the customer can visualize how the fire-brush-colored alpha khaki  720  and the new-British-colored alpha khaki  730  look in relation to the customer&#39;s avatar. For example, the avatar can include characteristics of the customer, such as skin tone, hair style, and so on. 
     According to various example embodiments, one or more of the methodologies described herein can facilitate the online purchase of garments. Additionally, embodiments can support the in-store purchase of garments using digital techniques to convey the same information without the user necessarily being online. 
     When these effects are considered in aggregate, one or more of the methodologies described herein can obviate a need for certain efforts or resources that otherwise would be involved in determining body measurements of a user. Efforts expended by a user in generating user-specific body models can be reduced by one or more of the methodologies described herein. Computing resources used by one or more machines, databases, or devices (e.g., within the network-based system  105 ) can similarly be reduced. Examples of such computing resources include processor cycles, network traffic, memory usage, data storage capacity, power consumption, and cooling capacity. 
       FIG.  8    is a block diagram illustrating components of a machine  800 , according to some example embodiments, able to read instructions  824  from a machine-readable medium  822  (e.g., a non-transitory machine-readable medium, a machine-readable storage medium, a computer-readable storage medium, or any suitable combination thereof) and perform any one or more of the methodologies discussed herein, in whole or in part. Specifically,  FIG.  8    shows the machine  800  in the example form of a computer system (e.g., a computer) within which the instructions  824  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  800  to perform any one or more of the methodologies discussed herein can be executed, in whole or in part. The network-based system  105 , devices  130 , and  140  can be examples of the machine  800 . 
     In alternative embodiments, the machine  800  operates as a standalone device or can be connected (e.g., networked) to other machines. In a networked deployment, the machine  800  can operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a distributed (e.g., peer-to-peer) network environment. The machine  800  can be a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a cellular telephone, a smartphone, a set-top box (STB), a personal digital assistant (PDA), a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  824 , sequentially or otherwise, that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute the instructions  824  to perform all or part of any one or more of the methodologies discussed herein. 
     The machine  800  includes a processor  802  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory  804 , and a static memory  806 , which are configured to communicate with each other via a bus  808 . The processor  802  can contain microcircuits that are configurable, temporarily or permanently, by some or all of the instructions  824  such that the processor  802  is configurable to perform any one or more of the methodologies described herein, in whole or in part. For example, a set of one or more microcircuits of the processor  802  can be configurable to execute one or more modules (e.g., software modules) described herein. 
     The machine  800  can further include a graphics display  810  (e.g., a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, a cathode ray tube (CRT), or any other display capable of displaying graphics or video). The machine  800  can also include an alphanumeric input device  812  (e.g., a keyboard or keypad), a cursor control device  814  (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, an eye tracking device, or another pointing instrument), a storage unit  816 , an audio generation device  818  (e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), and a network interface device  820 . 
     The storage unit  816  includes the machine-readable medium  822  (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions  824  embodying any one or more of the methodologies or functions described herein. The instructions  824  can also reside, completely or at least partially, within the main memory  804 , within the processor  802  (e.g., within the processor&#39;s cache memory), or both, before or during execution thereof by the machine  800 . Accordingly, the main memory  804  and the processor  802  can be considered machine-readable media (e.g., tangible and non-transitory machine-readable media). The instructions  824  can be transmitted or received over a network  34  via the network interface device  820 . For example, the network interface device  820  can communicate the instructions  824  using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)). 
     The machine-readable medium  822  can include a magnetic or optical disk storage device, solid state storage devices such as flash memory, or another non-volatile memory device or devices. The computer-readable instructions stored on the computer-readable storage medium are in source code, assembly language code, object code, or another instruction format that is interpreted by one or more processors. 
     In some example embodiments, the machine  800  can be a portable computing device, such as a smartphone or tablet computer, and have one or more additional input components  830  (e.g., sensors or gauges). Examples of such input components  830  include an image input component (e.g., one or more cameras), an audio input component (e.g., a microphone), a direction input component (e.g., a compass), a location input component (e.g., a global positioning system (GPS) receiver), an orientation component (e.g., a gyroscope), a motion detection component (e.g., one or more accelerometers), an altitude detection component (e.g., an altimeter), and a gas detection component (e.g., a gas sensor). Inputs harvested by any one or more of these input components can be accessible and available for use by any of the modules described herein. 
     As used herein, the term “memory” refers to a machine-readable medium able to store data temporarily or permanently and can be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium  822  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store the instructions  824 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing the instructions  824  for execution by the machine  800 , such that the instructions  824 , when executed by one or more processors of the machine  800  (e.g., the processor  802 ), cause the machine  800  to perform any one or more of the methodologies described herein, in whole or in part. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more tangible (e.g., non-transitory) data repositories in the form of a solid-state memory, an optical medium, a magnetic medium, or any suitable combination thereof. 
     The foregoing description, for purposes of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the present disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. 
     Throughout this specification, plural instances can implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations can be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations can be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component can be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules can constitute software modules (e.g., code stored or otherwise embodied on a machine-readable medium or in a transmission medium), hardware modules, or any suitable combination thereof. A “hardware module” is a tangible (e.g., non-transitory) unit capable of performing certain operations and can be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) can be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module can be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module can include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module can be a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware module can also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module can include software encompassed within a general-purpose processor or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) can be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, and such a tangible entity can be physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor can be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software (e.g., a software module) can accordingly configure one or more processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules can be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications can be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules can be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module can then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules can also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein can be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein can be at least partially processor-implemented, a processor being an example of hardware. For example, at least some of the operations of a method can be performed by one or more processors or processor-implemented modules. As used herein, “processor-implemented module” refers to a hardware module in which the hardware includes one or more processors. Moreover, the one or more processors can also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations can be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)). 
     The performance of certain operations can be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules can be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules can be distributed across a number of geographic locations. 
     Some portions of the subject matter discussed herein can be presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). Such algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities can take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities. 
     Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like can refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or any suitable combination thereof), registers, or other machine components that receive, store, transmit, or display information. Furthermore, unless specifically stated otherwise, the terms “a” or “an” are herein used, as is common in patent documents, to include one or more than one instance. Finally, as used herein, the conjunction “or” refers to a non-exclusive “or,” unless specifically stated otherwise.