OMNI-CHANNEL SIMULATED DIGITAL APPAREL CONTENT DISPLAY

Abstract

Techniques for an omni-channel approach for displaying simulated digital apparel content are presented herein. A machine can detect an available amount of a computing resource on a client device. A determination that the client device is to render only a three-dimensional body model, among a set of models that includes the three-dimensional body model and a three-dimensional garment model, and that a server is to render the three-dimensional garment model, may occur based on the detected available amount of the computing resource on the client device. The machine can provide the client device with the three-dimensional garment model draped on the three-dimensional body model. The machine can cause the server to render at least a portion of the three-dimensional garment model in accordance with the determination. The machine can cause the client device to render at least a portion of the three-dimensional body model in accordance with the determination.

Description

This application claims the priority benefit of: (1) U.S. Provisional Application No. 61/905,126, filed Nov. 15, 2013; (2) U.S. Provisional Application No. 61/904,263, filed Nov. 14, 2013; (3) U.S. Provisional Application No. 61/904,522, filed Nov. 15, 2013; (4) U.S. Provisional Application No. 61/905,118, filed Nov. 15, 2013; and (5) U.S. Provisional Application No. 61/905,122, filed Nov. 15, 2013, which applications are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present application relates generally to the technical field of data processing and specifically to three-dimensional (3-D) modeling and simulation.

BACKGROUND

Shopping for clothes and accessories in physical stores can be an arduous task and, due to traveling and parking, can be very time consuming. With the advent of online shopping, consumers may purchase clothing while staying home, via a computer or any electronic device connected to the Internet. Additionally, purchasing clothes online can be different from 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 virtual dressing room, can be a very important aspect of a successful and satisfying digital shopping experience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system for extracting body dimensions from two-dimensional photographs of garments, in accordance to certain example embodiments.

FIG. 2 is a block diagram illustrating an example file system, in accordance with certain example embodiments.

FIG. 3 is a block diagram illustrating an example simulation module, in accordance with certain example embodiments.

FIG. 4 is a flow diagram of a process for displaying a garment-draped avatar on a client device, in accordance with certain example embodiments.

FIG. 5 illustrates a user interface presented by a display module, in accordance with certain example embodiments.

FIG. 6 illustrates a user interface for a 360 degree view of a garment-draped avatar, in accordance with certain example embodiments.

FIG. 7 illustrates different viewpoints from the 360 degree view, in accordance with certain example embodiments.

FIG. 8 illustrates a virtual fitting room example of the garment-draped avatar, in accordance with certain example embodiments.

FIG. 9 illustrates various omni-channels, in accordance with certain example embodiments.

FIG. 10 illustrates an example of a tablet synchronized with a television with the virtual fitting room, in accordance with certain example embodiments.

FIG. 11 illustrates recommended sizes for different garments, in accordance with certain example embodiments.

FIG. 12 illustrates a sample triangle associated with a tessellated garment, in accordance with certain example embodiments.

FIG. 13 illustrates an example of a fit map, in accordance with certain example embodiments.

FIG. 14 illustrates another example of a fit map, in accordance with example embodiments.

FIG. 15 illustrates an online purchase of a garment, in accordance with certain example embodiments.

FIG. 16 is a high-level diagram for displaying an animation or a garment-draped avatar, in accordance with certain example embodiments.

FIG. 17 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

Techniques for an omni-channel approach to apparel and accessory e-commerce for displaying simulated digital apparel content are provided, in accordance with various example embodiments. The techniques described herein are specifically tailored for apparel and accessory commerce and retail activity. Omni-channel simulated digital apparel content includes displaying images of garments draped on a user-specific avatar across multiple access points (e.g., desktop, laptops, tablets, televisions, mobile phones, and store fronts). Additionally, Omni-channel simulated digital apparel content includes images related to the look and fit of garments on user-specific or generic 3-D human-form avatars. For example, the fit map can display the look and fit of garments on an avatar, and the fit map can be deployed for an omni-channel approach. The term “omni-channel,” for purposes herein, refers to a technique for displaying content (e.g., simulated digital apparel) on two or more devices (e.g., all available shopping channels) for a particular user, which may provide the user with a consistent and coherent consumer shopping experience as the user shifts attention from one device to another. A consistent and coherent shopping experience can be especially important when the multiple devices have differences in computing, input/output, communication, and security parameters. The techniques described herein can enable a fully-featured, consistent, coherent commerce experience to the customer, irrespective of which device or channel the customer engages with the business on.

The displayed images are generated by rendering an image of a garment-draped on an avatar. A machine configured by a suitable garment simulation module may perform such a rendering. The rendering includes determining one or more forces (e.g., gravitational force or tension force) in the garment when the garment is draped on the avatar, as the avatar performs one or more animations (e.g., walking or stooping). The determination (e.g., calculation) of such a force can be computationally intensive; therefore, the amount of rendering to be performed on a client device may be determined (e.g., by a machine configured by a suitable garment simulation module) based on the amount of a computing resource on the client device.

In some example embodiments, a simulation of the garment on an animated avatar (e.g., walking down a fashion show runway) can be displayed (e.g., by a server machine or a client device). For example, for each frame of an animation, a garment simulation module of a server machine may compute and store vertex positions of the simulated garment (e.g., vertices that constitute a model of the garment), as draped on a user's avatar, as well as compute and store one or more of the forces that may act on or be exerted on the simulated garment. Additionally, for each frame of the animation, the user's avatar can be rendered along with the corresponding garment under well-lit conditions. The rendering may generate and store a series of images of the simulated garment (e.g., with or without the user's avatar being depicted in any given image). The resolution of the image may be set dynamically (e.g., by the garment simulation module). For example, each image may be dynamically set to a resolution of 800×600 pixels based on a determination that the client device of the user can render only images 800×600 pixels in size or smaller for real-time or near-real-time rendering (e.g., with a frame rate of 15 frames per second or greater).

The series of images can be generated with varying resolutions for display on different client devices. For example, a range of image resolutions may be chosen and the corresponding sets of images rendered by a garment simulation module. As an alternative, each image may be rendered at a very high resolution (e.g., 10,000×8.000 pixels), and sub-sampling (e.g., to create a smaller resolution image) may be performed at run-time (e.g., by the garment simulation module).

The garment simulation module may display to customers static or dynamic information about the properties of garments across multiple access points (e.g., client devices) by using a mix of pre-processed content (e.g., processed on the server machine) and run-time content (e.g., processed on the client device). In some instances, the amount of run-time content to be processed on the client device can be pre-determined (e.g., by the garment simulation module) in order to provide a better user experience at a particular client device.

According to some example embodiments, an access module within a server machine may detect an available amount of computing resources on a client device. The available amount of computing resources can include communication resources (e.g., bandwidth). Based on the detected available amount of the computing resource, at least four different scenarios can occur. According to a first scenario, both the rendering of a three-dimensional (3-D) body model and the rendering of a 3-D garment model are processed on (e.g., performed by) the server machine (e.g., by using a garment simulation module or rendering module), and generated images based on both renderings are then transmitted to the client device. According to a second scenario, the rendering of the 3-D garment model is processed on the server machine, and the rendering of the 3-D body model is processed on the client device. According to a third scenario, the rendering of the 3-D body model is processed on the server, and the rendering of the 3-D garment model is processed on the client device. According to a fourth scenario, both the rendering of the 3-D body model and the rendering of the 3-D garment model are processed on the client device.

In the second scenario described above, a determination can be made by a garment simulation module (e.g., within the server machine) that the client device is to render only a 3-D body model, among a set of models that includes the 3-D body model and a 3-D garment model, and that a server machine is to render the 3-D garment model. Continuing with the second scenario, the garment simulation module may provide the client device with the 3-D garment model draped on the 3-D body model. Additionally, the garment simulation module may cause the server machine to render at least a portion of the 3-D garment model in accordance with the determination. Furthermore, the garment simulation module may cause the client device to render at least a portion of the 3-D body model in accordance with the determination. In some instances, the garment simulation module is the part of the server machine that renders the 3-D garment model. In other instances, the garment simulation module can cause a cloud-based server system (e.g., with multiple server machines) to render at least a portion of the 3-D garment model. The communication between the server and client can include transmission of the 3-D body model (e.g., 3-D body model, parts of the 3-D body model, or representative information of the 3-D body model) that can be used by a client device or a server to create the 3D body model. The representative information of the 3-D body model can include salient dimensions (e.g., bust, waist, hip, or height) or assets (e.g., photographs of the face or hair).

In some instances, the images transmitted from the server machine to the client device can include a group of images of the draped 3-D garment model. The group of images may illustrate multiple viewpoints around the draped 3-D garment model, such as a 360 degree view. Additionally, a second group of images can be transmitted from the server machine to the client device. The second group of images may form all or part of an animation of the garment-draped avatar. The quantity of images delivered from the server machine to the client device may be determined by the server machine based on the frame rate of the animation, and the frame rate may be determined based on the available amount of computing resources at the client device.

Additionally, simulated forces can be calculated and displayed on a fit map based on the garment model being draped and simulated on the body model. Using the fit map, a garment size can be recommended (e.g., by the garment simulation module or other recommendation engine within the server machine, the client device, or both) to a specific user based on the calculated simulated forces. Depending on the implementation, the simulated forces can be calculated by the garment simulation module, the rendering module, the server, or the client device.

Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may 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 may be practiced without these specific details.

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 may be practiced without these specific details.

FIG. 1 is a block diagram illustrating a network environment 100 in accordance with some example embodiments. The network environment 100 includes client devices (e.g., a client device 10-1, a client device 10-2, a client device 10-3) connected to a server 202 (e.g., server machine) via a network 34 (e.g., the Internet). The server 202 may include one or more processing units 222 (e.g., central processing units (CPUs)) for executing one or more software modules, programs, or instructions stored in a memory 236 and thereby performing one or more of the processing operations discussed herein; one or more communications interfaces 220; the memory 236; and one or more communication buses 230 for interconnecting these components. The communication buses 230 may include circuitry (e.g., a chipset) that interconnects and controls communications between system components. The server 202 also optionally includes a power source 224 and a controller 212 coupled to a mass storage 214. The network environment 100 optionally includes a user interface 232 that includes a display device 226 and a keyboard 228.

The memory 236 may 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 236 may 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 236 may optionally include one or more storage devices remotely located from the CPU 222. The memory 236, or alternately the non-volatile memory device within the memory 236, may be or include a non-transitory computer-readable storage medium. In some example embodiments, the memory 236, or the computer-readable storage medium of the memory 236, stores the following programs, modules, and data structures, or a subset thereof: an operating system 240; a file system 242; an access module 244; a garment simulation module 246; a rendering module 248; and a display module 250.

The operating system 240 is configured for handling various basic system services and for performing hardware-dependent tasks. The file system 242 can store and organize various files utilized by various programs. The access module 244 can communicate with client devices (e.g., the client device 10-1, the client device 10-2, or the client device 10-3) via the one or more communications interfaces 220 (e.g., wired, or wireless), the network 34, other wide area networks, local area networks, metropolitan area networks, and so on. Additionally, the access module 244 can access information for the memory 236 via the one or more communication buses 230.

The garment simulation module 246 is configured to generate a 3-D garment model. Additionally, the garment simulation module 246 can act on a generated a 3-D body model. U.S. Non-Provisional application Ser. No. 14/270,244 2014, filed May 5, 2014, titled “3-D DIGITAL MEDIA CONTENT CREATION FROM PLANAR GARMENT IMAGES,” which is incorporated herein by reference, further describes techniques for generating the 3-D body model based on salient dimensions, photos of fitting garments, or self-identification (e.g., user identifying representative bodies that are similar to the body type of the user).

Alternatively, the garment simulation module 246 can generate the 3-D body model based on the techniques described above (e.g., salient dimensions). For example, the garment simulation module 246 can cause the client device 10-1 or the server 202 to generate the 3-D garment model or the 3-D body model.

Additionally, the garment simulation module 246 can drape or cause a client device (e.g., client device 10-1) or the server 202 to drape the garment model on the body model. For example, the garment simulation module 246 can position the body model inside the garment model. Moreover, the garment simulation module 246 can calculate or cause the client device 10-1 or the server 202 to calculate one or more simulated forces acting on garment points associated with (e.g., corresponding to or included in) the garment model based on the positioning of the body model inside the garment model. A fit map can be determined using the calculated simulated forces. The fit map can be presented on a display of the client device 10-1. The garment simulation module 246 and the rendering module 248 can generate the fit map.

In some instances (e.g., the second scenario described above), the body measurement may not be transmitted by the client device 10-1 (e.g., for privacy reasons). In these instances, the garment simulation module 246 can cause the client device 10-1 to generate the 3-D body model. For example, the server 202 can generate the garment model using the garment simulation module 246 and the rendering module 248, and the client device 10-1 can generate the body model.

The rendering module 248 can generate an image of the 3-D garment model draped on the 3-D body model based on the calculated one or more simulated forces. The simulated forces can be calculated, by the rendering module 248, based on methods described herein (e.g., a three-spring implementation of a sample triangle with three vertices).

Additionally, the garment simulation module 246 or the rendering module 248 can determine the image resolution and the number of frames per second for the animation, based on computing resources of the client device. Furthermore, the garment simulation module 246 or the rendering module 248 can determine the amount of data (e.g., image data) that is pre-rendered, stored, and transferred to the client device 10-1 on the fly in contrast to the amount of data that is rendered on the client device 10-1.

The display module 250 can be configured to cause presentation of one or more generated images on a display of a device (e.g., client device 10-1). For example, the display module 250 may present the 3-D simulation discussed above on the display of mobile device. The presentation of the 3-D simulation may be based on the actions of the garment simulation module 246 and the rendering module 248.

The network 34 may be any network that enables communication between or among machines, databases, and devices (e.g., the server 202 and the client device 10-1). Accordingly, the network 34 may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network 34 may 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 34 may 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 34 may 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 server 202 and the client devices (e.g., the client device 10-1, the client device 10-2, and the client device 10-3) may each be implemented in a computer system, in whole or in part, as described below with respect to FIG. 17.

Any of the machines, databases, or devices shown in FIG. 1 may be implemented in a computer modified (e.g., configured or programmed) by software (e.g., one or more software modules) to be a special-purpose computer to perform one or more of the functions described herein for that machine, database, or device. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect to FIG. 17. As used herein, a “database” is a data storage resource and may 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 may be combined into a single machine, and the functions described herein for any single machine, database, or device may be subdivided among multiple machines, databases, or devices.

FIG. 2 further describes the memory 236 in the server 202, as initially described in FIG. 1. FIG. 2 includes an expanded depiction of the file system 242. The file system 242 may store one or more of the following data objects (e.g., files and databases): garment model files 251; extracted geometry files 252; extracted texture files 253; stitching information files 254; a garment template database 255; draping parameters files 256; simulation parameters files 257; and simulation result geometry files 258. FIG. 4 further describes operations using the data objects from FIG. 2.

FIG. 3 is a block diagram illustrating components of the garment simulation module 246, according to some example embodiments, as initially described in FIG. 1. The garment simulation module 246 is shown as including a boundary extraction module 261; a texture mapping module 262; a tessellation module 263; a stitching module 264; a draping module 265; and a simulation module 266, all configured to communicate with each other (e.g., via a bus, shared memory, or a switch).

Any one or more of the modules described herein may be implemented using hardware (e.g., one or more processors of a machine) or a combination of hardware and software. For example, any module described herein may configure a processor (e.g., among one or more processors of a machine) to perform the operations described herein for that module. Moreover, any two or more of these modules may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. Furthermore, according to various example embodiments, modules described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices.

Each of the above identified elements may be stored in one or more of the previously mentioned memory devices, and corresponds to a set of instructions for performing a function described above. The above identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise rearranged in various example embodiments. In some example embodiments, the memory 236 may store a subset of the modules and data structures identified above. Furthermore, the memory 236 may store additional modules and data structures not described above.

The number of servers used to implement the garment simulation module 246 and the rendering module 248 and how features are allocated among them will vary from one implementation to another, and may depend in part on the amount of data traffic that the network environment 100 handles during peak usage periods as well as during average usage periods. Additionally, the number of servers to implement the garment simulation module 246 and the rendering module 248 may depend in part on the available amount of computing resources on the client device (e.g., client device 10-1).

FIG. 4 is a flowchart representing a method 400 for displaying a garment-draped avatar, according to some 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 one or more servers. Each of the operations shown in FIG. 4 may correspond to instructions stored in a computer memory or computer-readable storage medium.

Operations in the method 400 may be performed by the server 202, using modules (e.g., garment simulation module 246, or rendering module 248) described above with respect to FIGS. 1-3. As shown in FIG. 4, the method 400 includes operations 410, 420, 430, 440, and 450. In certain example embodiments, the method 400 includes an operation for rendering a 3-D garment model and a 3-D body model using various devices. Method 400 may be performed to implement the second scenario that was previously mentioned, where the server 202 renders the 3-D garment model and the client device 10-1 renders the 3-D body model.

In operation 410, the garment simulation module 246 detects an available amount of computing resources on a client device. For example, the garment simulation module 246 may utilize the access module 244 to access the computing resources on client device 10-1 using the communications interface 220 via the network 34. The information relating to the detected computing resources may then be stored by the garment simulation module 246 in the simulation parameters files 257.

As used herein, a “computer resource” refers to any physical or virtual component of limited availability within a computer system. In some instances, one or more devices connected to a computer system can be a resource. Additionally, one or more internal system components (e.g., processor, memory, video card, display screen, and power) may be a resource. For example, processing speed or the resolution of the display screen may be a factor in determining the percentages of pre-processed and run-time content to be rendered by the server 202 or the client device 10-1. Moreover, virtual system resources include files, network connections, and memory areas. For example, the available amount of random access memory (RAM) or virtual memory can be a factor in determining the percentages of pre-processed and run-time content. Furthermore, the amount of available computing resources can correspond to computing resources in the graphics processing unit (GPU) of the client device 10-1. Furthermore, in some embodiments, the detecting of the available amount of the computing resource can include detecting the available capacity of a graphics processing unit within the client device 10-1.

In operation 420, based on the detected available amount of computing resources from operation 410, the garment simulation module 246 determines that the client device 10-1 is to render only a 3-D body model, among a set of models that includes the 3-D body model and a 3-D garment model. The 3-D body model can include the body of a user, the face of the user, the hair of the user and other features (e.g., make-up, and style). Additionally, the garment simulation module 246 may determine and that the server 202 is to render a 3-D garment model based on the detected available amount of computing resources. The garment simulation module 246 may configure at least one processor among the one or more processors (e.g., the CPU 222) to perform this determination.

For example, based on the detected available amount of computing resources at the client device 10-1, the garment simulation module 246 may determine that for an optimal user experience (e.g., animation displayed without delay during user interface), the garment model is to be rendered by the server 202 and the avatar is to be rendered by the client device 10-1. As previously mentioned, rendering can be computationally intensive, and therefore a partial image may be rendered (e.g., pre-processed) by the server 202 and transmitted to the client device 10-1.

In operation 430, the garment simulation module 246 provides the client device 10-1 with the 3-D garment model draped on the 3-D body model. The garment simulation module 246 may configure at least one processor among the one or more processors (e.g., the CPU 222) to provide the client device with the 3-D garment model draped on the 3-D body model.

For example, the 3-D garment model can include garment points (e.g., a set of points) that model or otherwise represent at least one surface of a garment. The garment simulation module 246 can drape the 3-D garment model on the 3-D body model by positioning at least a portion of the 3-D body model inside the 3-D garment model that includes the garment points.

In some instances, the body profile can be stored on a cloud server for the user to retrieve using the client device 10-1. In some other instances, the body profile can be stored on a third-party server (e.g., similar to server 202) of a merchant that a user can access when browsing a virtual fitting room. In yet some other instances, some or all aspects of the body profile can be stored in the client device 10-1 and not transmitted to any server (e.g., server 202) for privacy reasons.

In operation 440, the garment simulation module 246 causes the server 202 to render at least a portion of the 3-D garment model in accordance with the determination made in operation 420. In some instances, the server 202 renders the portion of the 3-D garment model without rendering any portion of the 3-D body model. According to an example embodiment, the garment simulation module 246 is part of the server that renders at least a portion of the 3-D garment model. According to another example embodiment, the garment simulation module 246 can cause a cloud-based server to render at least a portion of the 3-D garment model.

Rendering the 3-D garment model may include calculating a simulated force acting on a subset of the garment points based on the positioning of at least the portion of the 3-D body model within the 3-D garment model. A tessellation method can be used for calculating the simulated force, as later described in FIG. 13. In some instances, the simulated force includes a simulated gravitational force upon the draped 3-D garment model. The simulated force can also include a simulated tension force, elastic force, aerodynamic force, or friction force upon the draped 3-D garment model.

The rendering module 248 is configured to generate an image of the 3-D model based on one or more calculated simulated forces, and the generated image may be descriptive of the garment-draped avatar as influenced by these calculated simulated forces. The rendering module 248 may configure at least one processor among the one or more processors (e.g., the CPU 222) to generate the image using the draping module 265 and the simulation module 266. The garment-draped avatar may then be presented based on the one or more simulated forces. The presentation may be performed by digitally draping the 3-D model onto the avatar. In various example embodiments, the rendering involves taking data from all previous operations, combining the data, and inputting the data into a cloth simulation engine. Additionally, the simulation results may be stored by the rendering module 248 in the simulation result geometry files 258.

Moreover, the garment simulation module 246 can be further configured to calculate a simulated force acting on the subset of the garment points based on a material property of the garment. The material property of the garment may include a sheerness value, a linear stiffness value, or a bending stiffness value.

In operation 450, the garment simulation module 246 causes the client device 10-1 to render at least a portion of the 3-D body model in accordance with the determination made in operation 420. In some instances, the client device 10-1 renders the portion of the 3-D body model without rendering any portion of the 3-D garment model.

As illustrated in FIG. 5, the display module 250 presents to a user a garment (e.g., jeans) 510 draped on an avatar 520. The display module 250 may present the generated image 530 on a display of a device 540 (e.g., client device 10-1). The display module 250 can configure the user interface 232 for the presentation. The display module 250 can configure at least one processor among the one or more processors (e.g., the CPU 222) to present the generated image on the display of a mobile device (e.g., device 540).

In various example embodiments, the output is stored as a series of images. Both the resolution and number of images can be set dynamically. Additionally, the output can include other content, such as videos, 3-D objects, or text description of the simulation output.

As illustrated in FIG. 6, which is also referred to herein as the V.360 example, the garment may be displayed in a static body position. The garment 610 draped on an avatar 620 specific to a user can be rendered under well-lit conditions by revolving the viewpoint in a circle around a 3D model that combines the garment 610 and the avatar 620 of the user. A user can select a user interface button 630 for revolving the avatar 620 in a circle. Additionally, a projected image can be rendered for each of the viewpoints. As illustrated in FIG. 7, a first viewpoint 710, a second viewpoint 720, a third view point 730, and a fourth viewpoint 740 can be selected using the user interface button 630.

In some instances, the rendered images form all or part of a series of images (e.g., images that constitute an animation). Both the resolution of the images and the number of images in the series can be set dynamically (e.g., by the garment simulation module 246. In one example, the garment simulation module 246 may generate thirty images that are 12 degrees apart with a resolution of 800×600 pixels.

Furthermore, a whole range of image resolutions may be chosen by the garment simulation module 246 for the same content and the corresponding sets of images rendered. For example, as an alternative to the example above, 360 images with viewpoints that are one degree apart may be rendered at a high resolution (e.g., 10,000×8000 pixels). Additionally, these images can be sub-sampled to create a smaller resolution image to be performed at run-time.

FIG. 8 illustrates the virtual fitting room example. In the virtual fitting room example, customers can mix and match clothes (e.g., shirts 810 and pants 820) virtually in-store, and digitally try on inventory on an avatar 830. Similar to the V.360 example of FIG. 6, a user interface button 840 can be selected by a user to revolve the avatar 830 in a circle.

Additionally, as illustrated in FIG. 9, the omni-channel techniques described herein can provide a user 910 with a personal look-and-feel across multiple (e.g., all) media and channels (e.g., mobile device 920, tablet 930, laptop 940, desktop 950, social network 960, brick-and-mortar 970, digital wall in a store front 980, and digital monitor 990).

Furthermore, as illustrated in FIG. 10, the omni-channel techniques can be synchronized between or among different channels (e.g., devices belonging to the same user). For example, a user 1010 can be shopping online using a tablet 1020, while the image is also projected on a section 1030 of a television 1040. A television show can be displayed on another section of the television 1040. In this example, the garment simulation module 246 can determine the resolution, frame rate, and the amount of information to be rendered on each device (e.g., tablet 1020, or television 1040).

In various embodiments, as previously noted with respect to the four scenarios described above, the images may be: pre-rendered, stored and transferred on the fly; rendered on the client device; or a combination of both.

When the images are pre-rendered, stored and transferred to the client device on the fly, the corresponding images (e.g., thirty in the previous example) can be transferred and displayed to the user (e.g., user 910 or user 1010) at run-time. The images can be based on the user's dimensions and the garment of interest. The images can be transferred via an interactive interface, in which the user determines the image displayed by changing the view-point. The pre-rendered technique can have a very low computational overhead on the client device 10-1. For example, the client device 10-1 may just decompress and display the image, which can smoothly be performed even on low-end mobile devices. The size of the image can be chosen based on the amount of data transferred being within a given budget of transfer bandwidth to the client. Additionally, the size of the image can also be based on the display resolution of the client device 10-1. The pre-rendered implementation allows for an interactive experience on the client device 10-1 with high fidelity garment images for any kind of device. The series of images can be pre-rendered, stored, and transferred at run-time to the client device 10-1 to reduce computational overhead at the client device 10-1.

Alternatively, when the images are rendered on the client device 10-1, the relevant garment's vertex positions can be transferred to the client device 10-1, and, at run-time, the images are rendered on the client device 10-1. The relevant garment's vertex positions or parameters are transferred to the client device 10-1 at run-time in order for the rendering to occur at the client device 10-1. The client device 10-1 can use the same scene settings as in the pre-rendered implementation. This implementation may be optimized for a client device (e.g., client 10-1) with powerful computing resources.

In yet another implementation, the images can be based on a combination of the pre-rendering on the server 202, and rendering on the client device 10-1. A combination of pre-rendered images and relevant vertex positions or parameters can be sent to the client at run-time depending upon the desired computational overhead at the client device 10-1. A whole spectrum of intermediate solutions can exist using a combination of the pre-rendering on the server 202 and rendering on the client device 10-1. For example, the draped garment can be pre-rendered on the server 202, but the body can be rendered on the client device 10-1.

In the second and third scenarios, the user's avatar can be rendered on the client device 10-1, which can help strengthen the security aspect of virtual shopping using avatars since the body measurements of a user are stored in the client device 10-1. In such embodiments, the information about the user's dimensions is less likely to be compromised, because the information is not sent to a server (e.g., server 202). Additionally, rendering the avatar on the client device 10-1 can allow for a more customizable rendering experience for the user. Therefore, when the client device 10-1 has the specified amount of compute resources, the server 202 can deliver a whole range of customized personal garment rendering to help the user make a more informed purchase decision. In some instances, a secure transaction between the server and client (e.g., a one-time, session-specific transfer of personal information) can be used to convey private information about the user for purposes of simulation, rendering and display.

In some instances, the server 202 can provide the client device 10-1 with a partial image rendered from at least a portion of the 3-D garment model draped on the 3-D body model. The client device 10-1 can be configured to receive the partial image and generate a full image based on the partial image and by rendering at least the portion of the 3-D body model as described in operation 450.

According to some example embodiments, the garment simulation module 246 can be further configured to cause the server 202 to generate a group of images of the draped 3-D garment model. The group of images can depict multiple viewpoints around the draped 3-D garment model. Additionally, the generated group of images can have a quantity based on the detected available amount of the computing resource on the client device 10-1.

For example, when determined that the client device 10-1 is a high-powered (e.g., high-end processor) desktop computer, the server 202 can capture 360-degree images of the garment-draped avatar at every one degree to create a high-quality, 360-degree view of the garment-draped avatar. Alternatively, when it is determined that the client device 10-1 is a low-end mobile device, the server 202 can capture 36 images of the garment-draped on the avatar at every 10 degree to create a low-quality 360-degree view of the garment-draped avatar.

The rendered images can be generated using a GPU in the server 202 or the client device 10-1. Additionally, in some instance, the detection of the available amount of the computing resource can be based on the available capacity of a GPU within the client device 10-1.

According to some example embodiments, the server 202 can be further configured, by the garment simulation module 246, to transmit the image rendered in operation 440 at a specific resolution based on the detected available amount of the computing resource on the client device 10-1. For example, when it is determined that the available amount of the computing resource in the client device 10-1 includes a low resolution supported by a display, then the server 202 can transmit the rendered image with a supported low resolution (e.g., 800 pixels by 600 pixels).

According to some example embodiments, the server 202 can be further configured, by the garment simulation module 246, to transmit the images rendered in operation 440 with a specific frame rate at which an animation of the draped avatar is to be displayed. The frame rate may be determined based on the detected available amount of computing resources on the client device 10-1. The frame rate may be determined directly from the detected available amount of computing resources on the client device 10-1. For example, the higher the detected available amount of computing resources on the client device 10-1, the higher the frame rate may be set.

In the animation of the draped avatar example, the 3-D body model, the 3-D garment model, or both as a combined model, can have a first body position, and the garment simulation module 246 may be further configured to change (e.g., reposition) the 3-D body model, the 3-D garment model, or both, to a second body position. Additionally, the garment simulation module 246 can reposition at least a portion of the 3-D body model inside the garment points based on the change of the 3-D body model to the second body position, and calculate the simulated forces acting on a second subset of the garment points based on the repositioning. The frame rate can correspond to the number of images generated as the avatar repositions from the first body position to the second body position.

The rendering module 248 can be configured to animate the generated image as the 3-D body model moves from the first body position to the second body position, and the display module 250 can be configured to cause presentation of the animation on the client device 10-1. Additionally, the rendering module 248 can create a set of avatars (e.g., static, animated, or dynamic) for a content stage (e.g., fashion performance, 360° view, fit map, or suggest a size).

Additionally, the rendering module 248 can be further configured to animate the generated image as the 3-D body model moves from the first body position to the second body position, and subsequently to a third body position, which can be presented using the display module 250.

For example, the garment simulation module 246 and rendering module 248 can animate body meshes of an avatar under different animation sequences, such as swinging a golf club. In some instances, the system can animate the body meshes to perform a fashion presentation by superimposing motion-captured data (e.g., of different points on a body mesh) on the given mesh. Any kind of motion can be superimposed to form a catalogue of motions that a user can eventually choose from. For example, for a ten-second motion clip when the frame rate is set at 30 frames-per-second animation, the system can compute 300 frames (10 seconds times 30 frames) of the avatar.

In various example embodiments, for each of the above animation frames, the garment simulation module 246 and rendering module 248 can perform the stable garment simulation to compute the vertex positions of the garment. The garment positions can then be stored. Likewise, the forces can be computed and stored by the server 202 or client device 10-1 based on the available amount of computing resources on the client device 10-1. The garment simulation module 246 and rendering module 248 can exploit spatial coherence within consecutive frames to speed up the simulation run-time, for example by using the stable position of the previous frame as the starting position for the current frame and computing the resultant motion parameters of the garment.

For example, by simulating the garment model, the garment simulation module 246 can simulate a fashion experience. In some instances, simulation of the garment can include placing the garment around the body at an appropriate position, and running simulations based on calculations. The simulation can advance the position and other related variables of the vertices of the garment 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 246 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 reaches a steady state with a net force of zero.

Moreover, the precision can be adjusted to accommodate varying levels of desired accuracy of the garment model and can be based on available amount of computing resources on the client device 10-1. The precision can be automatically adjusted by the garment simulation module 246 and rendering module 248 based on the client device 10-1, 10-2, 10-3 (e.g., lower precision for a mobile device, higher precision for a large screen display). In some instances, the standard error of tolerance is a parameter that can be set. Tolerance can be measured by actual units of distance (e.g., 0.01 inches). Alternatively, tolerance can be measured in numbers of pixels.

According to some example embodiments, the garment simulation module 246 can be further configured to distort the 3-D garment model. For example, the rendering module 248 can distort the 3-D garment model by stretching or twisting the 3-D garment model. Distorting the digital garment model can generate 3-D models that are representative of the family of sizes of a garment typically carried and sold by retailers.

Distorting techniques can be used for recommending a size. For example, tops are usually distributed in a few generic sizes (e.g., XS, S, M, L, XL, or XXL). By computing the tension map for each size for the user's avatar, a recommended size can be suggested. The recommended size can be based on the size that fits the avatar's dimensions the closest with minimum distortion to the garment.

The distortion of the 3-D digital garment model can be uniform for the entire model (i.e., the entire model is grown or shrunk), or specific to individual zones (e.g., specific garment areas) with different distortions (e.g., scale factors) for the individual zones. Furthermore, the scaling of dimensions of the garments can be arbitrary (as in the case of creating a custom size), or can be according to specifications provided by a garment manufacturer. The specifications can be based on grading rules, size charts, actual measurements, or digital measurements.

As illustrated in FIG. 11, using a generated fit map, the garment simulation module 246 can determine the recommended size. Accordingly, the display module 250 can present an avatar 1110 with a recommended size 1120 to the user. Furthermore, the garment simulation module 246 can determine a recommended size based on the available garment sizes stored in the file system 242, or based on the user's current wardrobe 1130. By computing the tension map for each size for the user's avatar 1110, a recommended size 1120 can be suggested. The recommended size 1120 can be based on the size that fits the avatar's 1110 dimensions the closest with minimum distortion to the garment.

In addition to suggesting a recommended size, techniques for incorporating a user's fitting preferences (e.g., loose around the waist) are also described. Algorithms to compute a personalized size recommendation for the user can further be developed based on a user's buying and return pattern. In some instances, the personalized size recommendation can be based on dividing the body into zones and having a list of acceptable sizes for each zone. Furthermore, fit and size recommendation can be based on specific information about the class or type of garment. For example, given that yoga pants have a tight fit, when the class of garment is determined to be yoga pants, the garment simulation module 246 can infer that the garment has a tight fit based on parameters obtained from the manufacturer or a lookup table. Similarly, the garment simulation module 246 can infer that flare jeans have a loose fit at the bottom of the jeans.

For example, the body can be divided into zones. For a woman, the zones can include shoulders, bust, waist, hips, thighs, calves, and so on. For a given size of a garment of a certain category (e.g., jeans), the technique can determine if the garment fits based on the user's buying and return pattern. When the garment fits, the dimensions of the garment in each applicable zone can be added to a list of acceptable dimensions for the user. When the garment fits, the algorithm used by the garment simulation module 246 may assume that all the dimensions fit the user. Alternatively, when the garment does not fit (e.g., the user returns the garment), the dimensions of the garment in each applicable zone are added to a list of unacceptable dimensions and stored in a database, by the garment simulation module 246. Similarly, when the garment does not fit, the algorithm may assume that at least one of the dimensions did not fit the user.

A classifier (e.g., sequential minimization optimization (SMO)) may be used for each garment category implemented by the garment simulation module 246 based on the dimensions that either fit or do not fit the user. For a given new garment in a specific category, the garment simulation module 246 can predict the correct size based on the classifier and recommend the size to the user. Based on feedback (e.g., the user's buying and return pattern), the user's preference and the classifiers can be updated by the garment simulation module 246. In some instances, five to ten garments for a given category can help achieve over 90% accuracy on the correct user size. Accordingly, the number of garments to train and converge on user's preferences can be low (e.g., less than 10).

Now referring to FIG. 12, the 3-D garment model rendered by the server in operation 440 can be a tessellated 3-D garment model. The tessellated 3-D 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 by the tessellation module 263. The tessellated geometric shapes can be stored in the extracted geometry files 252. For example, a shirt 1210 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 3-D garment model. The garment points can include location information such as an x, y, and z position value.

The garment simulation module 246 can position at least a portion of the generated avatar inside the garment points. In some instances, positioning can include placing the garment on or around the avatar, given that the avatar may be fixed in some embodiments. In these instances, the garment can be stretched and deformed based on the simulation.

The simulations can be implemented through specific modules (e.g., the simulation module 266) stored in the memory 236. Some examples of implementations and equations are described below. For example, below is the system of equations to be used for a three-spring implementation of a sample triangle 1250 with three vertices (i.e., a vertex 1252, a vertex 1254, or a vertex 1256) associated with the tessellated shirt 1210, as illustrated in FIG. 12.

$\begin{array}{cc}{\mathrm{spring}}_{{\mathrm{force}}_1}=\left(\frac{k_s}{{\mathrm{restlength}}_1}\right)*\left(x_2-x_1-{\mathrm{restlength}}_1\right)*{\mathrm{spring}}_{{\mathrm{direction}}_1}+\left(\frac{k_d}{{\mathrm{restlength}}_1}\right)*{\mathrm{Dot}}_{\mathrm{Product}}\left(v_2-v_1,{\mathrm{spring}}_{{\mathrm{direction}}_1}\right)*{\mathrm{spring}}_{{\mathrm{direction}}_1}& \left(\mathrm{Equation}1\right)\\ {\mathrm{spring}}_{{\mathrm{force}}_2}=\left(\frac{k_s}{{\mathrm{restlength}}_2}\right)*\left(x3-x2-{\mathrm{restlength}}_2\right)*{\mathrm{spring}}_{{\mathrm{direction}}_2}+\left(\frac{k_d}{{\mathrm{restlength}}_2}\right)*{\mathrm{Dot}}_{\mathrm{Product}}\left(v3-v2,{\mathrm{spring}}_{{\mathrm{direction}}_2}\right)*{\mathrm{spring}}_{{\mathrm{direction}}_2}& \left(\mathrm{Equation}2\right)\\ {\mathrm{spring}}_{{\mathrm{force}}_3}=\left(\frac{k_s}{{\mathrm{restlength}}_3}\right)*\left(x1-x3-{\mathrm{restlength}}_3\right)*{\mathrm{spring}}_{{\mathrm{direction}}_3}+\left(\frac{k_d}{{\mathrm{restlength}}_3}\right)*{\mathrm{Dot}}_{\mathrm{Product}}\left(v1-v3,{\mathrm{spring}}_{{\mathrm{direction}}_3}\right)*{\mathrm{spring}}_{{\mathrm{direction}}_3}& \left(\mathrm{Equation}3\right)\end{array}$

• • Where k_s is the elastic spring constant, k_d is the damping spring constant, and each vertex has a position (x) and velocity (v).

In the equations above, when the denominator is a restlength value, a non-zero value can be used for zero-length springs. Additionally, the equations can use a visual restlength value when the denominator is not the restlength value, which in zero-length spring cases is 0. This allows for the system to handle zero-length springs without dividing by 0.

To further explain the equations above, a walkthrough of the equations is described. The garment simulation module 246 and rendering module 248 can maintain the positions and velocities of all the points that represent the garment. In future iterations, the simulator can update the positions of the points over time by computing the net force on each point at each instance in time. Then, based on the mass of the particle and the laws of motion, F=ma, an acceleration can be calculated. The acceleration determines a change in velocity, which can be used to update the velocity of each point. Likewise, the velocity determines a change in position, which can be used to update the positions. Therefore, at each point in the simulation, the simulator can compute the net force on each particle. The forces exerted on each particle can be based on a gravitational force, spring forces, or other forces (e.g., drag forces to achieve desired styling). The equation for gravitational force is F=mg, and the spring force is described above.

The spring force F has two components, an elastic component (e.g., the part of the equation multiplied by k_s) and a damping component (e.g., the part of the equation multiplied by k_d). The elastic component is related to the oscillation of the spring. The strength of the elastic force is proportional to the amount the spring is stretched from the restlength value, which can be determined by x₂-x₁ (e.g., the current length of the spring) minus the restlength value. For example, the more the spring is compressed or stretched, the higher the force pushing the spring to return to its rest state. Additionally, k_s is a spring constant that allows for scaling up/down the force based on the strength of the spring, which is then multiplied by the spring direction to give the force a direction (e.g., in the direction of the spring).

The damping component calculates the damping effect (e.g., heat being generated by the spring moving, drag). Damping can be drag force, where the higher the velocity, the higher the drag force. Accordingly, damping can be proportional to velocity. In the case of a spring, there can be two particles moving, so instead of a single velocity, the simulator computes a relative velocity between the two endpoints (e.g., v₂-v₁ in FIG. 12). For example, the larger the relative velocity, the faster the points are moving apart or coming close together, and as a result the larger the damping force (e.g., the damping is proportional to relative velocity). Additionally, k_d is the damping spring constant to scale the damping force either up or down, which can be multiplied by the spring direction to give the force a direction.

The resultant output can be stored or displayed to a user. In some instances, for each of the bodies, the garment simulation module 246 can capture the position of the vertices at the end of the simulation, and store the information in a database. For a mesh with K vertices, a total of 3K numbers are stored (the x, y, and z positions for each vertex). These constitute the look of the given garment on any given body.

In various example embodiments, at the steady state of each simulation, the garment simulation module 246 can also compute the forces being exerted in the springs (e.g., edges) of the mesh. For example, for an edge between two vertices (e.g., V₁ and V₂), the resultant force on V₁ (and correspondingly V₂) equals:

F(V₁)=k(V₁,V₂)*Delta(V_1—V₂), where  (Equation 4)

• • k(V₁, V₂) is the spring constant of the spring joining V₁ and V₂ (e.g., a function of the material property of the garment); and
  • Delta(V_1—V₂) is a velocity-dependent force function based on the change in position vectors for V₁, V₂ as compared to their original rest state. These forces can then be accumulated for each vertex to compute the resultant force.

In various example embodiments, for each of the bodies, the garment simulation module 246 can store the resultant force on each vertex (e.g., 1252, 1254, 1256) in the simulation result geometry files 258. The resultant force on each vertex can serve as a measure of the tightness (e.g., for large force magnitude) or looseness in different regions of the garment. The resultant force computed can be interpreted as a stress, pressure, or compression on the garment. Additionally, the resultant force can be a representation of a force felt by the body at the corresponding point or region.

FIGS. 13-14 illustrate the resultant forces in a fit map. For example, the tight regions can be depicted using warm colors, and the loose regions depicted using cool colors.

Techniques for displaying a fit map on a garment for the same static position are provided, in accordance with example embodiments. The fit map can illustrate tension forces, inferred force, or pressure on the body. The fit map can show and convey regions of the garment that can be tight or loose on a user. This additional information can aid the user in making an informed purchase decision without physically trying on the garment.

As illustrated by FIG. 13, the garment model can be draped on the body model. According to some example embodiments, the method 400 of FIG. 4 can further include operations where the garment simulation module 246 is configured to generate a fit map based on the calculated simulated forces, and the display module 250 can present the garment-draped avatar with a generated fit map 1310 as illustrated in FIG. 13.

A fit map can show display cues. For example, a set of output forces can be chosen. Each output force can correspond to a range of forces (e.g., tight, loose) that can be displayed to the user. Additionally, style information can be presented based on the force. For example, loose or tight clothing may convey some style information. FIG. 13 shows an example of a fit map 1310 with color display cues. As illustrated in FIG. 13, the display cues can be overlaid, by the rendering module 268, on the rendered garment itself. As illustrated, the generated fit map 1310 can be based on a magnitude of the calculated simulated forces. For example, when the magnitude of the calculated simulated forces is high, the fit map 1310 can label that section of the garment as a tight section 1320. Alternatively, a loose section 1330 occurs when the magnitude of the calculated simulated forces is low.

Furthermore, the fit map can convey derivative information such as the relative differences in force, style, and fit between two garments. For example, a user can use the derivative information from the fit map to select between the two sizes or style. In some instances, the derivative information can be presented using colors or cues.

As illustrated in FIG. 14, a fit map 1410 can be generated by assigning a color to a garment point (e.g., a vertex in the tessellated garment model). The color values can be determined based on the calculated simulated force. Each color corresponds to a range of forces. For each vertex, the corresponding color can be computed and stored. The color information can be rendered from revolving viewpoints around the body to compute a color-coded tension map.

For example, in the fit map 1410, each vertex of the shape (e.g., triangle) is assigned a red-green-blue (RGB) value. In some instances, the generated fit map 1410 can be colored based on a magnitude of the calculated simulated forces. For example, sections of the garment that are tight around the body of a user can be colored red 1420, while loose sections of the garment can be colored blue 1430. Thus in the triangulation method, each triangle potentially has three different RGB values. The rest of the points of the triangle can then be interpolated. Interpolation allows for the RGB values of the remaining points in the triangle to be filled in using a linear combination method (e.g., the points of the triangle are weighted based on the distance to the three vertices and the RGB values are assigned accordingly).

As previously mentioned, for both of the above examples, the output can be stored as a series of images. Both the resolution and number of images can be set dynamically. According to one example embodiment, the garment simulation module 246 can generate thirty images that are 12 degrees apart with a resolution of 800×600 pixels. Furthermore, a whole range of image resolutions may be chosen, and the corresponding sets of images rendered.

In various embodiments, omni-channel marketing involves delivering a consistent, correlated, connected look and feel to all interactions between a business (e.g., brand) and a consumer. For example, in apparel retail, consumers can interact with a brand or retailer via a physical brick-&-mortar store, online web store, smart TV, mobile application, email message, digital kiosk, digital wall or digital or physical catalog. Using the techniques described herein, omni-channel marketing can make sure that each experience on any access channel with the brand reinforces the brand image in the minds of the customer.

Conversely, customers can use multiple devices at various times as they progress with the impression-discovery-research-purchase-feedback cycles of commerce. Delivering a consistent look and feel and coherent information improves the customer's satisfaction and loyalty. In the present disclosure, we describe a method (e.g., method 400) of delivering personalized simulated apparel content with a consistent look, feel and quality across any channel of engagement between the brand and the customer.

In various embodiments, the role of brick-&-mortar stores is evolving to incorporate the use of digital devices and media in a physical store, in keeping with the evolution of retail in an omni-channel engagement world. More broadly the traditional roles of a store being both a product discovery center as well as a fulfillment center are getting split up, with the physical store front focusing on a product discovery experience using a combination of digital technologies and physical products.

FIG. 15 illustrates an example embodiment of the retail scenario. For example, e-commerce technologies like the virtual fitting technologies described herein can be used in store in conjunction with technologies like UPC code scanning either on a customer's mobile device or a fixed asset like a digital wall, dressing room or kiosk. Customers can mix and match clothes 1510 virtually in store, or digitally try-on 1520 inventory and concept garments from other locations. Another implementation of this technology is in-store personalized style 1530 and size recommendations either based on inventory in the store or within the retailer or brand's entire catalog. Virtual fitting technology and size and style recommendation technology can be triggered based on inputs such as location in a store, time of the year, or any other relevant variable. Further, such recommendations can be triggered by a customer action, such as the scan of a bar code or location-based trigger. Additionally, the recommendation can be triggered by the owner or operator of the store. Further, the in-store virtual fitting experience can be performed by a customer for another customer, such as a friend of a family member, regardless of location (e.g., co-located in the store, or away from the store). Furthermore, the technology can employ the body information as well as the style and fit preference for the digitally augmented in-store experience.

FIG. 16 is a high-level diagram for displaying an animation or a garment-draped avatar, in accordance with certain example embodiments. The garment simulation module 246 can take as input tessellation using an access module 244, and can output 3-D models of clothing on an avatar using a rendering module 248. The simulation module 246 can use digitization 1610, modeling 1620, simulation 1630, and automated 1640 techniques to generate a 3-D simulation. The 3-D simulation can include a catwalk 1650, a 360-degree view 1660, a recommended size 1670 suggestion, or a virtual fitting room 1680 experience. The simulation module 246 can move points around to fit an avatar based on a simulated force (e.g., friction, stitching force). Additionally, based on this modeling, the points are connected via springs and can be stretched based on a simulated force (e.g., gravity, material property of garment). The simulation module 246 and rendering module 248 can solve a system of equations, given that the equations are all inter-connected. In one example, the system of equations can be based on the spring force on each vertex. According to various example embodiments, one or more of the methodologies described herein may facilitate the online purchase of garments.

When these effects are considered in aggregate, one or more of the methodologies described herein may obviate a need for certain efforts or resources that otherwise would be involved in determining body measurements of a user from garment images. Efforts expended by a user in generating user-specific body models may 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 environment 100) may similarly be reduced since the different scenarios can be dependent on the available amount of computing resources on the client device or server. Examples of such computing resources include processor cycles, network traffic, memory usage, data storage capacity, power consumption, and cooling capacity.

FIG. 17 is a block diagram illustrating components of a machine 1700, according to some example embodiments, able to read instructions 1724 from a machine-readable medium 1722 (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. 17 shows the machine 1700 in the example form of a computer system (e.g., a computer) within which the instructions 1724 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1700 to perform any one or more of the methodologies discussed herein may be executed, in whole or in part. The server 202 can be an example of the machine 1700.

In alternative embodiments, the machine 1700 operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 1700 may 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 1700 may 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 1724, 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 1724 to perform all or part of any one or more of the methodologies discussed herein.

The machine 1700 includes a processor 1702 (e.g., a CPU, a 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 1704, and a static memory 1706, which are configured to communicate with each other via a bus 1708. The processor 1702 may contain microcircuits that are configurable, temporarily or permanently, by some or all of the instructions 1724 such that the processor 1702 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 1702 may be configurable to execute one or more modules (e.g., software modules) described herein.

The machine 1700 may further include a graphics display 1710 (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 1700 may also include an alphanumeric input device 1712 (e.g., a keyboard or keypad), a cursor control device 1714 (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, an eye tracking device, or other pointing instrument), a storage unit 1716, an audio generation device 1718 (e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), and a network interface device 1720.

The storage unit 1716 includes the machine-readable medium 1722 (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions 1724 embodying any one or more of the methodologies or functions described herein. The instructions 1724 may also reside, completely or at least partially, within the main memory 1704, within the processor 1702 (e.g., within the processor's cache memory), or both, before or during execution thereof by the machine 1700. Accordingly, the main memory 1704 and the processor 1702 may be considered machine-readable media (e.g., tangible and non-transitory machine-readable media). The instructions 1724 may be transmitted or received over the network 34 via the network interface device 1720. For example, the network interface device 1720 may communicate the instructions 1724 using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)).

The machine-readable medium 1722 may include a magnetic or optical disk storage device, solid state storage devices such as flash memory, or other non-volatile memory device or devices. The computer-readable instructions stored on the machine-readable medium 1722 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 1700 may be a portable computing device, such as a smartphone or tablet computer, and have one or more additional input components 1730 (e.g., sensors or gauges). Examples of such input components 1730 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 1730 may 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 1722 able to store data temporarily or permanently and may 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 1722 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 1724. 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 1724 for execution by the machine 1700, such that the instructions 1724, when executed by one or more processors of the machine 1700 (e.g., the processor 1702), cause the machine 1700 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 may 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 may 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 may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may 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 may 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 may 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) may 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 may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may 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) may 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 may 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 may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software (e.g., a software module) may 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 may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may 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 may 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 may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may 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 may 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 may 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 may be at least partially processor-implemented, a processor being an example of hardware. For example, at least some of the operations of a method may 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 may 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 may 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 may 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 may 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 may be distributed across a number of geographic locations.

Some portions of the subject matter discussed herein may 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 may 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 may 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.

Claims

1. A method comprising:

detecting an available amount of a computing resource on a client device;

determining that the client device is to render only a three-dimensional body model, among a set of models that includes the three-dimensional body model and a three-dimensional garment model, and that a server is to render the three-dimensional garment model, the determining being based on the detected available amount of the computing resource on the client device;

providing the client device with the three-dimensional garment model draped on the three-dimensional body model;

causing the server to render at least a portion of the three-dimensional garment model in accordance with the determining; and

causing the client device to render at least a portion of the three-dimensional body model in accordance with the determining.

2. The method of claim 1, wherein the client device renders the portion of the three-dimensional body model without rendering any portion of the three-dimensional garment model, and wherein the three-dimensional body model includes a face and hair.

3. The method of claim 1, wherein the server renders the portion of the three-dimensional garment model without rendering any portion of the three-dimensional body model.

4. The method of claim 1, wherein the three-dimensional garment model includes garment points that represent a surface of a garment, the method further comprising:

draping the three-dimensional garment model on the three-dimensional body model by positioning at least a portion of three-dimensional body model inside the three-dimensional garment model that includes the garment points.

5. The method of claim 4, further comprising:

calculating a simulated force acting on a subset of the garment points based on the positioning of at least the portion of the three-dimensional body model.

6. The method of claim 5, wherein the simulated force includes a simulated gravitational force upon the draped three-dimensional garment model.

7. The method of claim 5, wherein the simulated force includes a simulated tension force upon the draped three-dimensional garment model.

8. The method of claim 5, further comprising:

selecting a size from a set of available sizes for a garment based on the simulated force calculated based on the positioning of at least the portion of the three-dimensional body model.

9. The method of claim 1, further comprising:

causing the server to generate a group of images of the draped three-dimensional garment model, the group of images depicting multiple viewpoints around the draped three-dimensional garment model.

10. The method of claim 9, wherein with the generated group of images has a quantity based on the detected available amount of the computing resource on the client device.

11. The method of claim 1, further comprising:

providing the client device with a partial image rendered by the server from at least a portion of the three-dimensional garment model draped on the three-dimensional body model.

12. The method of claim 11, wherein the client device is configured to receive the partial image and generate a full image based on the partial image and by rendering at least the portion of the three-dimensional body model.

13. The method of claim 11, wherein the partial image is generated using a graphics processing unit in the server.

14. The method of claim 11, further comprising determining a resolution of the partial image based on the detected available amount of the computing resource on the client device.

15. The method of claim 11, further comprising determining a frame rate at which an animation of the draped three-dimensional model is to be displayed, the frame rate being determined based on the detected available amount of the computing resource on the client device.

16. The method of claim 1, wherein the detecting of the available amount of the computing resource includes detecting an available capacity of a graphics processing unit within the client device.

17. The method of claim 1, further comprising:

generating and providing a partial image that depicts a garment in which regions of different tightness have different colors, the partial image being generated by rendering at least a portion of the three-dimensional garment model draped on the three-dimensional body model.

18. The method of claim 17, wherein the generating of the partial image includes calculating a simulated force that acts on a first region of the three-dimensional garment model but not on a second region of the three-dimensional garment model.

19. A system comprising:

a garment simulation module configured to: detect an available amount of a computing resource on a client device; determine that the client device is to render only a three-dimensional body model, among a set of models that includes the three-dimensional body model and a three-dimensional garment model, and that a server is to render the three-dimensional garment model, the determining being based on the detected available amount of the computing resource on the client device;

a rendering module comprising one or more processors and configured to: provide the client device with the three-dimensional garment model draped on the three-dimensional body model; cause the server to render at least a portion of the three-dimensional garment model in accordance with the determination; and cause the client device to render at least a portion of the three-dimensional body model in accordance with the determination.

20. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

detecting an available amount of a computing resource on a client device;

determining that the client device is to render only a three-dimensional body model, among a set of models that includes the three-dimensional body model and a three-dimensional garment model, and that a server is to render the three-dimensional garment model, the determining being based on the detected available amount of the computing resource on the client device;

providing the client device with the three-dimensional garment model draped on the three-dimensional body model;

causing the server to render at least a portion of the three-dimensional garment model in accordance with the determining; and

causing the client device to render at least a portion of the three-dimensional body model in accordance with the determining.