3-DIMENSIONAL DIGITAL GARMENT CREATION FROM PLANAR GARMENT PHOTOGRAPHS

Abstract

Techniques for generating and presenting a three-dimensional garment model are presented herein. A communication interface can be configured to receive images, where all visible parts of the garment may be captured by the received images. A garment creation module can be configured to generate partial shapes of the garment based on the received images. Additionally, the garment creation module can determine a type of garment by comparing the generated partial shapes to a database of reference garment shapes. Furthermore, the garment creation module can generate a three-dimensional garment model by joining the partial shapes based on the determined type of garment, and can tessellate the generated three-dimensional garment model. A user interface can be configured to present the tessellated three-dimensional garment model on a three-dimensional body model.

Description

TECHNICAL FIELD

The present application relates generally to the technical field of three-dimensional (3-D) modeling and, in one specific example, to 3-D garment modeling for online shopping.

BACKGROUND

Shopping for clothes in conventional (e.g., non-online) can be an arduous task and, due to travelling 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 in comparison to purchasing clothes in a store. One difference is the lack of a physical dressing room to see if and how an article of clothing fits the particular consumer. Since different consumers can have different dimensions, seeing how an article of clothing fits, by use of a dressing room, can be a very important aspect of a successful and satisfying shopping experience.

The systems and methods described in the present disclosure attempt to provide solutions to the problems presented above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for three-dimensional (3-D) digital garment creation from planar garment photographs, in accordance with embodiments of the present disclosure.

FIG. 2 is a block diagram illustrating an exemplary file system, in accordance with embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating an exemplary 3-D digital garment creation module, in accordance with embodiments of the present disclosure.

FIG. 4 is a flow diagram of a process for 3-D digital garment creation, according to certain embodiments of the present disclosure.

FIG. 5 is a flow diagram continuing the process for 3-D digital garment creation from FIG. 4, according to certain embodiments of the present disclosure.

FIGS. 6-8 illustrate examples of garments in a garment template database, in accordance with embodiments of the present disclosure.

FIG. 9 illustrates a method for creating 3-D digital jeans based on a front image and a back image of the jeans and presenting the digital jeans on a 3-D body model, in accordance with embodiments of the present disclosure.

FIG. 10 illustrates method for creating a 3-D digital dress based on a front image and a back image of the dress and presenting the digital dress on a 3-D body model, in accordance with embodiments of the present disclosure.

FIG. 11 illustrates an example for joining partial shapes to generate a 3-D digital shirt, in accordance with embodiments of the present disclosure.

FIG. 12 illustrates another example for joining partial shapes to generate a 3-D hooded sweatshirt without joining some of the edges, in accordance with embodiments of the present disclosure.

FIG. 13 illustrates a sample triangle associated with the tessellated garment, in accordance with embodiments of the present disclosure.

FIG. 14 illustrates an example of a triangulation method, in accordance with embodiments of the present disclosure.

FIG. 15 illustrates a method for calibrating the size of the garment based on a calibration object, in accordance with embodiments of the present disclosure.

FIG. 16 is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium and perform any one or more of the methodologies discussed herein.

DESCRIPTION OF EMBODIMENTS

Example systems and methods for 3-dimensional (3-D) digital garment creation from one or more planar garment images are described. The systems can include instructions to produce a 3-D garment model using one or more planar garment images (e.g., photographs). Additionally, the systems can present the garment model on a 3-D body model based on various body shapes/dimensions, the tension or force in the garment draped on a body, and how the garment flows as the body performs actions.

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 embodiments, examples 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. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

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

Memory 236 includes 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; and 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. Memory 236 may optionally include one or more storage devices remotely located from the CPU(s) 222. Memory 236, or alternately the non-volatile memory device(s) within memory 236, comprises a non-transitory computer readable storage medium. In some embodiments, memory 236, or the computer readable storage medium of memory 236, stores the following programs, modules and data structures, or a subset thereof: an operating system 240; a file system 242; a network communications module 244; and a 3-D digital garment creation module 246.

The operating system 240 can include procedures 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 network communications module 244 can communicate with client devices (e.g., client device 10-1, client device 10-2, client device 10-3) via the one or more communications interfaces 220 (e.g., wired, wireless), the network 34, other wide area networks, local area networks, metropolitan area networks, and so on.

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., Wi-Fi network or 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., client device 10-1, client device 10-2, client device 10-3) may each be implemented in a computer system, in whole or in part, as described below with respect to FIG. 16.

Any of the machines, databases, or devices shown in FIG. 1 may be implemented in a general-purpose 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. 16. 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.

Although FIG. 1 shows a system 100, FIG. 1 is intended more as a functional description of the various features which may be present in a set of servers than as a structural schematic of the embodiments described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some items shown separately in FIG. 1 could be implemented on single servers and single items could be implemented by one or more servers.

FIG. 2 further describes the exemplary memory 236 in server 202, as initially described in FIG. 1. FIG. 2 includes an expanded depiction of exemplary file system 242. File system 242 may include one or more of the following files: input image photo files 251; extracted geometry files 252; extracted texture files 253; stitching information files 254; garment template database 255; draping parameter files 256; simulation parameter files 257; and simulation result geometry files 258. FIGS. 4-5 further describe operations using the files from FIG. 2.

FIG. 3 is a block diagram illustrating components of the 3-D digital garment creation module 246, according to some example embodiments, as initially described in FIG. 1. The 3-D digital garment creation 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). FIGS. 4-5 further describe operations using the modules from FIG. 3.

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 (i.e., 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 embodiments. In some embodiments, memory 236 may store a subset of the modules and data structures identified above. Furthermore, memory 236 may store additional modules and data structures not described above.

The actual number of servers used to implement a 3-D digital garment creation module 246 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 system handles during peak usage periods as well as during average usage periods.

FIGS. 4-5 are flowcharts representing a method 400 for 3-dimensional digital garment creation from one or more planar garment images, according to certain embodiments of the present disclosure. 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 FIGS. 4-5 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 described above with respect to FIG. 3, As shown in FIGS. 4-5, the method 400 includes operations 410, 420, 430, 440, 450, 460, 470 and 480. Optionally, method 400 can include an operation for calibrating the size of the garment and an operation for applying a texture map on the digital garment.

The computer readable storage medium 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 computer readable storage medium are in source code, assembly language code, object code, or other instruction format that is interpreted by one or more processors.

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.

At operation 410, 3-D digital garment creation module 246 can receive a first image depicting a first view of a garment. The first image (e.g., planar garment photographs) can include input image photo files 251. For example, a user can capture the front view of a pair of jeans using a camera on a mobile device and transmit the image, using a receiver on the mobile device, to the 3-D digital garment creation module 246.

Similar to the operation 410, 3-D digital garment creation module 246 can receive a second image depicting a second view of a garment at operation 420. In some instances, two received images can suffice, if all visible parts of the garment are captured in the set of received images. In some other instances, one or more other images (e.g., third image, fourth image) may be received by the 3-D digital garment creation module in order to capture all visible part of the garment.

For example, a user can capture the front and the back view of a pair of jeans with just two images using a camera and transmit the image to the 3-D digital garment creation module 246. The first and second images can be received from a client device (e.g., client device 10-1) or a third party vendor using network 34 (e.g., Bluetooth, cellular, internet).

A first and a second side of a garment can be determined using the first and second image received at operations 410 and 420. The images received at operations 410 and 420 can be stored in the input image photo files 251.

At operation 430, 3-D digital garment creation module 246 can generate a first partial shape of the garment based on the received first image using boundary extraction module 261.

At operation 440, 3-D digital garment creation module 246 can generate a second partial shape of the garment based on the received second image using boundary extraction module 261. The partial shapes generated at operations 430 and 440 can be stored in the extracted geometry files 252. Optionally, when texture information is obtained from the received images, the texture information associated with the generated partial shapes can be stored in the extracted texture files 253.

In some instances, generating the partial shape can be based on determining an identified boundary or outline of the garment. The boundary can be determined by identifying a discrete set of points (e.g., set of vertices) using a boundary detection algorithm.

One example of a boundary detection algorithm can be to determine the color-range of the background of the image by averaging out pixel values at the boundary (e.g., first row, first column, last row, last column) of the input image. The background color can be determined to be B (i.e., B_RED, B_GREEN, B_BLUE). Additionally, a pre-determined threshold value (t) can be chosen. The threshold value can be set by the user or calculated by the system (e.g., system 100). All pixel values in the received images that are within a range of the background color (i.e., B_RED+/−t, B_GREEN+/−t, B_BLUE+/−t) are interpreted as background pixels, and hence not part of the garment. Having identified each pixel value as either foreground (i.e., part of garment) or background, for each row of pixels, the pixel values where there is a transition between foreground and background can be identified as the contour/garment boundary pixels. Using these boundary pixels, an outline can be used to generate a partial shape of the garment.

In another example of a boundary detection algorithm, fir each row of pixels, the intensity (or color value) at each pixel is compared to the intensity (or color value) of the previous pixel. For a pre-determined threshold, once the difference between consecutive pixel values exceeds the threshold, the identified pixels can be classified as boundary pixels. In some instances, the intensity values for the foreground and background can be assigned via the scan line method. The scan line method includes traversing individual pixels and assigning the designation of background to the colors that match the outer edges of the photograph. In another instances, the boundary can be identified (e.g., extracted) using a gradient calculation method. In the gradient calculation method, differences in pixel color and intensity are calculated between adjacent pixels. A boundary can be identified when the differences are above a predetermined threshold value (e.g., sharp difference in pixel color and/or intensity between adjacent pixels). In yet other instances, the boundary can be determined using both the scan line method and the gradient calculation method. Using both methods can allow for a more accurate identification of the boundary.

Generating the partial shapes can include creating a continuous curve using the identified boundary. As mentioned, the identified boundary can be a discrete set of points. The discrete set of points can be a set of vertices associated with pixels that have been identified as boundary points using a boundary detection algorithm. The curve can be created by joining the discrete set of points that are determined to be boundaries of the garment and then running a smoothing function to eliminate outliers. Additionally, the curve can be modified based on a garment template from the garment database. The curve can be smoothed out by eliminating noise (e.g., remove outliers from the data), For example, noise can refer to the artifacts in image acquisition (e.g., lighting, image compression). Hence, the process of noise removal can help create a smooth edge instead of a jagged edge.

Moreover, the precision can be adjusted to accommodate varying levels of desired accuracy of the created digital garment and can be based on computation power. The precision can be automatically adjusted by the system based on the client device (e.g., lower precision or mobile device, higher precision for 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 number of pixels.

Furthermore, accuracy parameters can be received (e.g., from a user) or determined (e.g., by 3-D digital garment creation module 246) to help identify the boundary of the garment. Accuracy parameters can include, but are not limited to, extracted geometry files 252, extracted texture files 253, stitching information files 254 and garment template database 255.

Optionally, texture and optical properties can be determined from the images (e.g., photographs) at operations 430 and 440 in stored in the extracted texture files 253. The texture information can be used to determine the material properties of the garment and can be used to generate the texture map. The material properties of the garment can be used for calculating the simulated forces on the 3-D garment at operation 480. Furthermore, the material properties can be matched to the garment template database 255 at operation 450 in order to determine the type of garment using the texture mapping module 262. For example, the system can identify pleats in a garment when every part of the garment is captured in one of the input images. Moreover, the material property can be extracted even if the images of the garment are stretched or sheared. The optical properties can be used during the optional operations of applying a texture map to the 3-D digital garment.

At operation 450, the 3-D digital garment creation module 246 can determine a type of garment by comparing the generated first and second partial shapes to a database of reference garment shapes using the garment template database 255 and the stitching module 264.

The garment template database 255 can include stitching information files 254. The stitching information files include which corresponding edges in the partial shapes are connected to each other. The draping parameters files 256 can also extracted from the garment template database 255. Additionally, the simulation parameters files 257 can also extracted from the garment template database 255.

FIGS. 6-8 illustrate examples of garments in a garment template database 255 used in operations 450, in accordance with embodiments of the present disclosure. For example, in FIG. 6, the jeans garment template 505 can include information such as the number of panels 510, stitching information 515 of the jeans, body placement parameters 520 of the jeans, draping parameters 525, simulation parameters 530, and other relevant information associated with the jeans garment template.

In another example, in FIG. 7, the sleeveless dress garment template 535 can include information such as the number of panels 540, stitching information 545 of the dress, body placement parameters 550 of the dress, draping parameters 555, simulation parameters 560, and other relevant information associated with the sleeveless dress garment template.

FIG. 8 illustrates an exemplary garment template database 255, which includes the jeans garment template 505 of FIG. 6, and the sleeveless dress template 535 of FIG. 7. Additionally, the garment template database 255 can include other garment templates.

Returning back to method 400, at operation 450, the 3-D digital garment creation module 246 can extract the identified boundary from the partial shapes and match the shape of the extracted boundary to known databases of shapes (e.g., garment template database 255) of categorized garments (e.g., jeans garment template 505, sleeveless dress garment template 535) in order to determine the type of garment.

At operation 460, the 3-D digital garment creation module 246 can generate a 3-D garment module by joining the first partial shape and the second partial shape based on the determined type of garment. The generated 3-D garment module can include a first group of vertices based on the set of vertices from the partial shapes. The first group of vertices can be the outline of the 3-D garment module when the partial shapes have been joined (e.g., stitched).

For example, as illustrated in FIG. 9, two images (e.g., photographs) of the front of the jeans and the back of the jeans can be sufficient when all parts of the garment are captured in the images. Using the two images, the 3-D digital garment creation module 246 can generate a first partial shape corresponding to the front of the jeans 610 and a second partial shape corresponding to the back of the jeans 620 at operations 430 and 440. Then, at operation 450, the 3-D digital garment creation module 246 can determine that the received images are images of a pair of jeans by comparing the generated partial shapes to the jeans garment template 505 in the garment template database 255. Moreover, based on the determination that the garment is a pair of jeans, at operation 460 the 3-D digital garment creation module 246 can join the partial shapes to generate a 3-D pair of the digital jeans 630. As will be further described at operation 470, the digital jeans 630 can be tessellated. Furthermore, the 3-D pair of digital jeans 630 can be presented on an avatar 640 at operation 480. The avatar 640 can have similar dimensions to the user that is interested in purchasing the jeans. Moreover, a fit map 650 corresponding to the tightness and/or looseness of the jeans on the avatar 640 can be presented to the user.

In another example, as illustrated in FIG. 10, two partial shapes of the front of a dress 710 and the back of a dress 720 are generated based on received images. The 3-D digital garment creation module 246 can generate a 3-D digital dress 730 with only two received images and present the 3-D digital dress 730 on an avatar 740. Similar to the example in FIG. 9, only two images may be necessary because all parts of the dress are captured in the two images. If all parts on the dress are not captured in the two received images, then more images may be required to generate the 3-D digital garment. Additionally, the avatar 740 can illustrate how the dress looks and feels by demonstrating a fashion presentation 750 (e.g., catwalk) with the 3-D digital dress 730. Alternatively, the avatar 740 can illustrate how the dress looks and feels by demonstrating a lifestyle presentation. The lifestyle presentation can show the garments in use in everyday activities.

Continuing with operation 460, and as illustrated in FIG. 11, the 3-D digital garment creation module 246 can join the partial shapes by digitally stitching together the shapes of the different sides of the garment to produce a garment model. The different sides may include a first side 810 and a second side 820 of the garment. For example, after generating a partial shape for front and back of the garment at operations 430 and at 440, the two partial shapes can be joined (e.g., stitched together digitally) as illustrated by the joining of the digital shirt 830. As previously mentioned, the digital garment can be presented on an avatar 840.

In some instances, when all parts of the garment are not captured in the first two received images, more than two sides can be joined to generate the 3-D garment. For example, in FIG. 11, the different sides may also include a third side 850 and a fourth side 860 of the garment.

Continuing with operation 460, in some embodiments, a digital stitch can be based on a line connecting two points. 3-D digital garment creation module 246 can align the front side and the back side versions of the garment by looking for similar analogous points on a side using the other side as a reference. The 3-D digital garment creation module 246 can recognize which edges to join by matching a particular garment shape to a particular entry already stored in the garment template database 255. An exemplary garment database can hold entries for different garments (e.g., jeans garment template 505, sleeveless dress garment template 535, blouse garment template, sweater garment template, shirt garment template). In some embodiments, if the shape does not match a previously stored entry in the basic garment database, then algorithms may be needed to provide guidance in sewing the sides together for the particular new garment shape. Alternatively, the intervention can be automated. The shape can then be stored as a new entry into the basic garment database.

In some instances, the stitch length can be set to zero, thus producing a zero length spring. A good stitching job can be represented by setting the stitch length to zero. Additionally, in some instances, the 3-D digital garment creation module 246 can prevent bad stitching jobs by inhibiting stitching the front and the back of a garment where the stitches are long and can be seen. Accordingly, a stitch length equal to zero or close to zero length allows for a better digitally stitched garment at operation 460. However, setting the stitch length to zero or close to zero can be computationally intensive, because the simulation may need to solve a large number of equations. To illustrate this exemplary simulation, when using equations representing springs, based on Hooke's law, the denominator may be the length of the spring. Therefore, when the length of the spring has been set to zero, the equation solver has to solve equations with a zero in the denominator, which is not possible. Accordingly, another more computationally intensive formula for representing a spring, without using a denominator equal to zero, may be used.

Additionally, 3-D digital garment creation module 246 can recognize which points to stitch and which points not to stitch based on a specific algorithm. For example, in FIG. 12, 3-D digital garment creation module 246 recognizes that first edge 910 and second edge 920 are not supposed to be joined (e.g., stitched) because those edges are intended to be an opening (e.g., opening to allow a user's head to fit through). Therefore, when the digital hooded sweatshirt 930 is generated at operation 460, the first edge 910 and second edge 920 are not joined. In some instances, the 3-D digital garment creation module 246 can recognize which edges to not join by matching a particular garment shape to a particular entry already stored in a basic garment database.

Returning to method 400, at operation 470, 3-D digital garment creation module 246 can tessellate the generated 3-D garment model by adding a second group of vertices to the generated 3-D garment model using the tessellation module 263. As illustrated in FIG. 13, tessellation can include breaking down (e.g., tiling) a garment into many tessellated geometric shape (e.g., sample triangle 950) to generate a tessellated garment 940. For example, the shirt can be tessellated with triangles (e.g., about 20,000 triangles when triangle edge is around 1 centimeters), and the vertices (i.e., vertex 952, vertex 954, vertex 956) of the triangles can be the second group of vertices in the generated 3-D garment model. The vertices of the triangles can give location information of certain points in the material. The location information can be an x, y and z position value, and the location position can be independent of color and design of the garment.

Tessellation can be used to determine the location of certain points in the material of the garment. The certain points in the material of the garment can be represented by planar shapes. For example, the interior of the boundary of the garment can be filled with a plurality of similar geometric shapes. The points used for the tessellation can be based on the vertices of the shape. The shapes for the tessellation can be triangles, given that triangles are an efficient way (e.g., less computational power, faster tessellation speed) of representing a tessellated garment.

Furthermore, the points of the tessellated geometric shape can bend outside the shape, but not within. For example, if the tessellated shape is a triangle, different triangles can be folded over other triangles, but a triangle cannot be folded within itself. In other words, the triangle itself remains planar. In such example, the three vertices of the triangle determine the three points. An example tessellation can be an extracted shape (e.g., a shirt shape) being filled with a plurality of triangles, each with edges that can be calibrated (e.g., 1 cm). Thus, each point on the shirt can be approximated or located by reference to the nearest vertex on the most proximate triangle to the location of the determined position. In some embodiments, the triangles are equilateral triangles to maximize efficiency. In some arrangements, tessellation is consistent for each garment and thus, in the example, the same 1 cm edge triangle shape is used for tessellation of all extracted shapes. Alternatively, different tessellation shapes are used for different extracted shapes. Furthermore, tessellation can refer to the location of points of material and can be independent of the color and design of the garment.

Continuing with operation 470, according to some embodiments, the Delaunay triangulation method can be the triangulation method used for tessellation. In the Delaunay triangulation method, each iteration of the triangulation can try to maximize the minimum angle of the triangles in order to make close-to-uniform triangles. By maximizing the angles, the system ensures that none of the triangles are too skewed, and ensures the physical simulation runs efficiently.

For example, as illustrated in FIG. 14, Triangulation-2 980 can be better than Triangulation-1 970 for tessellation. As shown in FIG. 14, the minimum angle 982 in Triangulation-2 980 is greater than the minimum angle 972 in Triangulation-1 970, As a result, the triangles in Triangulation-2 980 are close-to-uniform, and can help with the draping and simulating the digitized garment.

In various embodiments, data of tessellation and boundary can be compatible with single instruction multiple data (SIMD). SIMD can be a type of vector processor that uses the same instruction on multiple elements. SIMD compatibility can ensure that the code is consistent with the hardware. Making the processes SIMD friendly can allow for utilization of the hardware in a more efficient manner because current hardware includes processors, or processors with SIMD units, Additionally, the tessellation can be done in parallel (e.g., performing the tessellation using multiple SIMD units in parallel) in order to increase the tessellation speed, and the simulation of the garment under different scenarios.

Optionally, method 400 can include an operation for calibration, as illustrated in FIG. 15. The first and/or second images received at operations 410 and/or 420 can include an object (e.g., credit card) with a known size for the 3-D digital garment creation module 246 to calibrate the boundary of the garment. In various embodiments, identifying the boundary can include computing shape and size of the garment.

In FIG. 15, the calibration object 1010 can be placed near the garment before the image is taken such that the one or more planar garment images (e.g., image of the front side of the jeans 1020, image of the back side of the jeans 1030) also include the calibration object 1010. In some instances, the calibration object 1010 can be placed on the garment, where the calibration object 1010 is clearly visible in the photograph but not distinct from the garment itself. A square object may be a better object for calibration because of the straight lines, four equal sides and four equal angles.

The calibration technique in method 400 can determine the actual dimensions of the garment depicted in the one or more photographs. The calibration technique can be achieved through proportional comparison by utilizing any object of standard size (e.g., grid paper of standard size, a standard credit card, a CD).

Calibration can assign an x, y, z position value to each pixel. Given the garment is laid out on a planar surface, the system may need the relative position of three points to compute the calibration (or projection mapping from image to object space). For example, using the calibration object 1010, the system can extract the four corner points, and given the dimensions of the calibration object 1010, the system has enough information to compute the calibration. Based on the calibration, the system can present the garment on an avatar 1040 and display properties 1050 (e.g., rise measurement, inseam measurement, hips measurement, thigh measurement, calf measurement) associated with the garment. Similarly, with a grid paper as the calibration object 1010, the system can use the relative positions of three points to compute this calibration.

Optionally, method 400 can further include applying a texture map to the 3-D garment model. In one or more arrangement, the 3-D digital garment creation module 246 applies a texture map to the tessellated three-dimensional garment model. The texture map can include assigning a color to a vertex in the second group of vertices based the received first image. The color values can be extracted from the received images, or alternatively, may be assigned from a different image (e.g., a texture swatch applied to the whole garment). Since a shape of the garment has already been determined using the operations described above, texture mapping can give the garment a texture and color. The texture can be represented as color. For example, in texture mapping, each vertex of the shape (e.g., triangle) is assigned a red-green-blue-alpha (RGBA) value. Alpha can be the transparency value. Thus in the triangulation method, each triangle has potentially three different RGBA values per triangle. The rest of the points of the triangle can then be interpolated. Interpolation allows for the RGBA 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 RGBA values are assigned accordingly). The interpolated values can be extracted from the received image, or alternatively, may be assigned from a different image (e.g., a texture swatch applied to the whole garment).

At operation 480, 3-D digital garment creation module 246 can present the tessellated 3-D garment model on a body model using the draping module 265 and the simulation module 266. The tessellated 3-D garment model is presented based on a simulated force. The presentation can be done by digitally draping the tessellated 3-D garment model onto a 3-D body model. In some embodiments, 3-D digital garment creation module 246 can put the digitally stitched garment generated at operation 470 onto a standard body, as illustrated by avatars 640 and 740. In various embodiments, operation 480 involves taking data from all previous operations and combining them and inputting them into a cloth simulation engine. Additionally, the simulation results from operation 480 can be stored in the simulation result geometry files 258.

Optionally, method 400 can include generating multiple sizes of the same garment by scaling or distorting the 3-D digital garment model. Scaling or distorting the 3-D 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. Alternatively, scaling or distorting the 3-D digital garment model can generate a specific sized version of 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. Additionally, 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. The specifications can be based on grading rules, size charts, actual measurements, and/or digital measurements.

As illustrated in FIGS. 9-10, a cloth engine can take as input tessellation and material properties and can output 3-D models of clothing on avatars 640 and 740. The cloth engine can move the points around to fit a 3-D body model 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 cloth engine 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.

Various operations described in method 400 can be implemented through specific modules stored in memory 236. Some examples of implementations and equations are described below. For example, below is the system of equations to be used with method 400 for a three-spring implementation of a sample triangle 950 with three-vertices (i.e., vertex 952, vertex 954, vertex 956) associated with a tessellated garment 940, as illustrated in FIG. 13.

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

• • 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 state that the simulator (e.g., 3-D digital garment creation module 246) can maintain is the positions and velocities of all the points that represent the garment. As the simulator moves forward in time, the simulator can update the position 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, the simulator can use the equation based on the laws of motion, f=ma, to calculate an acceleration. The acceleration determines a change in velocity, which can be used to update the velocity of each point. Likewise, the velocity determines the 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 (i.e., part of equation multiplied by k_s) and a damping component (i.e., part of equation multiplied by k_d). The elastic component calculates 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 (i.e., 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 (i.e., 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/damping 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. 13). 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 (i.e., the damping is proportional to relative velocity). Additionally, k_d is the damping spring constant to scale the damping force up/down, which can multiply by the spring direction to give the force a direction.

According to various example embodiments, one or more of the methodologies described herein may facilitate the online purchase of garments. Moreover, one or more of the methodologies described herein may facilitate the visualization of a garment on a 3-D body model using 3-D digital garment creation module 246.

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 digitalizing the garment from images. Efforts expended by a user in generating 3-D 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 system 100) may similarly be reduced. Examples of such computing resources include processor cycles, network traffic, memory usage, data storage capacity, power consumption, and cooling capacity.

FIG. 16 is a block diagram illustrating components of a machine 1200, according to some example embodiments, able to read instructions 1224 from a machine-readable medium 1222 (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. 16 shows the machine 1200 in the example form of a computer system (e.g., a computer) within which the instructions 1224 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1200 to perform any one or more of the methodologies discussed herein may be executed, in whole or in part. Server 202 can be an example of machine 1200.

In alternative embodiments, the machine 1200 operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 1200 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 1200 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 1224, 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 1224 to perform all or part of any one or more of the methodologies discussed herein.

The machine 1200 includes a processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory 1204, and a static memory 1206, which are configured to communicate with each other via a bus 1208. The processor 1202 may contain microcircuits that are configurable, temporarily or permanently, by some or all of the instructions 1224 such that the processor 1202 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 1202 may be configurable to execute one or more modules (e.g., software modules) described herein.

The machine 1200 may further include a graphics display 1210 (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 1200 may also include an alphanumeric input device 1212 (e.g., a keyboard or keypad), a cursor control device 1214 (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, an eye tracking device, or other pointing instrument), a storage unit 1216, an audio generation device 1218 (e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), and a network interface device 1220.

The storage unit 1216 includes the machine-readable medium 1222 (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions 1224 embodying any one or more of the methodologies or functions described herein. The instructions 1224 may also reside, completely or at least partially, within the main memory 1204, within the processor 1202 (e.g., within the processor's cache memory), or both, before or during execution thereof by the machine 1200. Accordingly, the main memory 1204 and the processor 1202 may be considered machine-readable media (e.g., tangible and non-transitory machine-readable media). The instructions 1224 may be transmitted or received over the network 34 via the network interface device 1220. For example, the network interface device 1220 may communicate the instructions 1224 using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 1200 may be a portable computing device, such as a smart phone or tablet computer, and have one or more additional input components 1230 (e.g., sensors or gauges). Examples of such input components 1230 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 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 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 1222 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 instructions 1224. 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 1224 for execution b the machine 1200, such that the instructions 1224, when executed by one or more processors of the machine 1200 (e.g., processor 1202), cause the machine 1200 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.

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 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.

It will be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.

Claims

1. A system comprising: one or more processors;a communication interface configured to: receive a first image depicting a first view of a garment; andreceive a second image depicting a second view of the garment;a garment creation module that configures at least one processor among the one or more processors to: generate a first partial shape of the garment based on the received first image;generate a second partial shape of the garment of the garment based on the received second image;determine a type of garment by comparing the generated first and second partial shapes to a database of reference garment shapes;generate a three-dimensional garment model by joining the first partial shape and the second partial shape based on the determined type of garment, the generated three-dimensional garment model including a first group of vertices; andtessellate the generated three-dimensional garment model by adding a second group of vertices to the generated three-dimensional garment model; anda user interface configured to present the tessellated three-dimensional garment model on a three-dimensional body model, the tessellated three-dimensional garment model being presented based on a simulated force.

2. The system of claim 1, wherein the garment creation module configures the one or more processors to tessellate the generated three-dimensional garment model by tessellating the generated three-dimensional garment model into triangles, wherein vertices in the triangles correspond to points that are interior to the generated three-dimensional garment model.

3. The system of claim 2, wherein overlaps and gaps do not exist between the triangles of the tessellated three-dimensional garment model.

4. The system of claim 2, wherein a triangle among the triangles of the tessellated three-dimensional garment model has a minimum angle, and wherein the garment creation module further configures the one or more processors to tessellate the generated three-dimensional garment model by maximizing the minimum angle of the triangle.

5. The system of claim 1, wherein the garment creation module configures the one or more processors to apply a texture map to the tessellated three-dimensional garment model by assigning a color to a vertex in the second group of vertices based the received first image.

6. The system of claim 1, wherein the garment creation module configures the one or more processors to determine actual dimensions of the garment using a reference object placed near the garment in the first image.

7. The system of claim 1, wherein the simulated force includes at least one of a gravitational force, a frictional force, or aerodynamic drag.

8. The system of claim wherein the user interface is configured to present the tessellated three-dimensional garment model on the three-dimensional body model based on a material property of the garment, wherein the material property includes at least one of sheerness, linear stiffness, or bending stiffness.

9. The system of claim 1, wherein the garment creation module configures the one or more processors to generate the first partial shape of the garment by: calculating a difference in color between a pixel and its adjacent pixels within the first image; anddetermining that the pixel is on a first edge of the first partial shape of the garment based on the difference transgressing a predetermined threshold value.

10. The system of claim 9, wherein the garment creation module configures the one or more processors to join the first and second partial shapes by aligning the first edge of the first partial shape with a second edge of the second partial shape.

11. The system of claim 9, wherein the garment creation module configures the one or more processors to smooth the first edge of the first partial shape into a smoothed curve by removing artifacts in the first image, wherein the artifacts is due to a lighting effect in the first image and an image compression of the first image.

12. The system of claim 1, wherein the garment creation module configures the one or more processors to generate a first flat-panel as the first partial shape and a second flat-panel as the second partial shape.

13. The system of claim 12, wherein the garment creation module configures the one or more processors to generate the three-dimensional garment model by joining at least a portion of the first flat-panel with at least a portion of the second flat-panel.

14. The system of claim 1, wherein all visible parts of the garment are captured in the received first image and received second image.

15. The system of claim 1, wherein the communication interface is further configured to receive a third image depicting a third view of a garment, wherein all visible parts of the garment is captured by the received first image, received second image and received third image.

16. The system of claim 1, wherein the garment creation module configures the one or more processors to generate different sized garment models by distorting the three-dimensional garment model.

17. A method comprising: receiving a first image depicting a first view of a garment;receiving a second image depicting a second view of the garment;generating a first partial shape of the garment based on the received first image;generating a second partial shape of the garment of the garment based on the received second image;determining, by one or more processors, a type of garment by comparing the generated first and second partial shapes to a database of reference garment shapes;generating a three-dimensional garment model by joining the first partial shape and the second partial shape based on the determined type of garment, the generated three-dimensional garment model including a first group of vertices;tessellating the generated three-dimensional garment model by adding a second group of vertices to the generated three-dimensional garment model; andpresenting the tessellated three-dimensional garment model on a three-dimensional body model, the tessellated three-dimensional garment model being presented based on a simulated force.

18. The method of claim 17, further comprising: applying a texture map to the tessellated three-dimensional garment model by assigning a color to a vertex in the second group of vertices based on the received first image.

19. The method of claim 17, wherein the presenting the tessellated three-dimensional garment model on the three-dimensional body model is further based on calculations using a material property of the garment, wherein the material property includes at least one of sheerness, linear stiffness, or bending stiffness.

20. A non-transitory machine-readable storage comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising: receiving a first image depicting a first view of a garment;receiving a second image depicting a second view of the garment;generating a first partial shape of the garment based on the received first image;generating a second partial shape of the garment of the garment based on the received second image;determining a type of garment by comparing the generated first and second partial shapes to a database of reference garment shapes;generating a three-dimensional garment model by joining the first partial shape and the second partial shape based on the determined type of garment, the generated three-dimensional garment model including a first group of vertices;tessellating the generated three-dimensional garment model by adding a second group of vertices to the generated three-dimensional garment model; andpresenting the tessellated three-dimensional garment model on a three-dimensional body model, the tessellated three-dimensional garment model being presented based on a simulated force.