US20150134493A1

US20150134493A1 - Three-dimensional digital media content creation from high-fidelity simulation

Info

Publication number: US20150134493A1
Application number: US14/449,120
Authority: US
Inventors: Jonathan Su; Mihir Naware; Jatin Chhugani
Original assignee: Individual
Current assignee: eBay Inc
Priority date: 2013-11-14
Filing date: 2014-07-31
Publication date: 2015-05-14
Also published as: US11145118B2; US10068371B2; US20150130795A1; US9378593B2; US9953460B2; US20160292915A1; US20150134495A1; US10410414B2; US20150134494A1; US20180350140A1; US20150134302A1; US20200090402A1

Abstract

Techniques for three-dimensional garment simulation are presented herein. An access module can be configured to access a three-dimensional garment model of a garment. The garment model can include garment points that represent a surface of the garment. Additionally, a three-dimensional body model can be generated based on body measurements, body scanning, or garment information. A processor can be configured by a garment module to position at least a portion of the generated three-dimensional body model inside the garment points, and calculate one or more simulated forces acting on a subset of the garment points. Moreover, a rendering module can be configured to generate an image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more simulated forces. Furthermore, a display module can be configured to present the generated image on a display of a device.

Description

CLAIM OF PRIORITY

This application claims the benefit of priority to: (1) U.S. Provisional Patent Application Ser. No. 61/904,263, filed Nov. 14, 2013; (2) U.S. Provisional Patent Application Ser. No. 61/905,126, filed Nov. 15, 2013; (3) U.S. Provisional Patent Application Ser. No. 61/904,522, filed Nov. 15, 2013; (4) U.S. Provisional Patent Application Ser. No. 61/905,118, filed Nov. 15, 2013; and (5) U.S. Provisional Patent Application Ser. 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 three-dimensional (3-D) modeling and simulation and, in one specific example, to 3-D garment simulation for online shopping.

BACKGROUND

Shopping for clothes in physical stores can be an arduous task and, due to travelling and parking, can be very time consuming. With the advent of online shopping, consumers 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 example system fir three-dimensional (3-D) digital garment creation from planar garment photographs, in accordance with example embodiments.

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

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

FIG. 4 is a flow diagram of a process for 3-D digital media content simulation, according to certain example embodiments.

FIG. 5 illustrates a sample triangle associated with a tessellated garment, in accordance with example embodiments.

FIG. 6 illustrates a method for presenting digital jeans on a 3-D body model, in accordance with example embodiments.

FIG. 7 illustrates method for presenting a digital dress on a 3-D body model, in accordance with example embodiments.

FIG. 8 illustrates an example of a fit map, in accordance with example embodiments.

FIG. 9 illustrates another example of a fit map, in accordance with example embodiments.

FIG. 10 illustrates an example of distorting the 3-D digital garment model, in accordance with example embodiments.

FIG. 11 illustrates how the garment looks and feels by demonstrating a lifestyle presentation, in accordance with example embodiments.

FIG. 12 illustrates how the garment looks and feels by demonstrating a fashion show presentation, in accordance with example embodiments.

FIG. 13 illustrates a user interface for recommending a size to a user, in accordance with example embodiments.

FIG. 14 illustrates a user interface for inputting body parameters, in accordance with example embodiments.

FIG. 15 illustrates different body models based on the body parameters, in accordance with example embodiments.

FIG. 16 is a block diagram illustrating an example digital content media simulation, in accordance with example embodiments.

FIG. 17 illustrates a method of facilitating the online purchase of garments, in accordance with example embodiments.

FIG. 18 illustrates a method of facilitating the online purchase of garments, in accordance with example embodiments.

FIG. 19 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 simulating a three-dimensional (3-D) garment model on a 3-D body model are described. Additionally, the systems can present the garment model on a 3-D body model based on various body shapes/dimensions, the tension or three in the garment draped on a body, and how the garment flows as the body performs actions.
The system also contains instructions to create one or more human-like body models having a plurality of salient body parameters. The system also contains instructions to simulate the garment model on at least one of the body models. The simulation is done by placing the garment model on the body model, running a physically accurate physics-based simulation that advances the position and other related variables of the vertices of the garment model while obeying the laws of physics, garment material properties, and body-garment interactions.
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, thr 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, 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 example embodiments.
FIG. 1 is a block diagram illustrating a system 100 in accordance with example embodiments. The system 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 via a network 34 (e.g., the Internet). The server 202 typically includes one or more processing units (CPUs) 222 for executing modules, programs, or instructions stored in a memory 236 and thereby performing processing operations; one or more communications interfaces 220; the memory 236; and one or more communication buses 230 for interconnecting these components. The communication buses 230 optionally 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 system 100 optionally includes a user interface 232 comprising a display device 226 and a keyboard 228.
The 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. 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, comprises 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 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 access module 244 can communicate with client devices (e.g., the client device 10-1, the client device 10-2, the 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. 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 can generate a three-dimensional body model based on the body measurement of a person. Additionally, the garment simulation module 246 can position the body model inside the garment model. The garment model can be accessed at operation 410. Moreover, the garment simulation module can calculate simulated forces acting on garment points associated with the garment model based on the positioning of the body model inside the garment model. Using the calculated simulated forces, a fit map can be determined. The fit map can be used to tell a user the recommend size to wear based on the determination.
The rendering module 248 can generate an image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more simulated forces. The simulated forces can be calculated based on methods (e.g., three-spring implementation of a sample triangle with three vertices) described herein.
The display module 250 can be configured to cause presentation of the generated image on a display of a device. For example, the display module can present the three-dimensional simulation on the display of mobile device. The three-dimensional simulation can 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, 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. 19.
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. 19. 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 include one or more of the following files: 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 files 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 via a bus, shared memory, or a switch). FIG. 4 further describes operations using the modules from FIG. 3. Additionally, 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 the files (stitching information files 254) from FIG. 2 and the modules (e.g., boundary extraction module 261) 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 (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 actual number of servers used to implement the garment simulation 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 100 handles during peak usage periods as well as during average usage periods.
FIG. 4 is a flowchart representing a method 400 for three-dimensional digital media content simulation, according to example embodiments. The method 400 is governed by instructions stored in a computer-readable storage medium and that are executed by one or more processors of 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 described above with respect to FIGS. 1-3. As shown in FIG. 4, the method 400 includes operations 410, 420, 430, 440, 450, 460, and 470. Optionally, the method 400 can include an operation for determining the size of the garment and an operation for applying a fit map to the garment.
At operation 410, the access module 244 can access, from a database, a three-dimensional garment model of a garment, the three-dimensional garment model including garment points that represent a surface of the garment. The garment model can be received using the communications interface 220 via the network 34. The accessed three-dimensional garment model of a garment can be stored in the garment model files 251.
For example, the accessed three-dimensional garment model can be a tessellated three-dimensional garment model. The tessellated three-dimensional garment model can includes a group of vertices associated with points on the surface of the garment. The tessellated 3-D garment model of the garment can be received using the communications interface 220 via the network 34.
The garment points can be generated using a tessellation technique by the tessellation module 263. Tessellation can file a garment into many tessellated geometric shapes to generate the tessellated garment with garment points. The tessellated geometric shapes can be stored in the extracted geometry files 252. Optionally, when texture information is obtained from the accessed information at operation 410, the texture information can be stored in the extracted texture files 253. U.S. Non-Provisional application Ser. No. 14/270,244 2014, which is incorporated herein by reference, describes techniques for generating a tessellated 3-D garment model.
For example, a shirt can be tessellated with triangles (e.g., about 20,000 triangles when a triangle edge is around 1 centimeter), and the vertices of the triangles can be the garment points of the three-dimensional garment model. The garment points can include location information such as an x, y, and z position value. Additionally, the location information can be independent of the color and design of the garment.
At operation 420, the access module 244 can access a body measurement of a person. In some instances, the access module 244 can access a plurality of body measurements. The body measurement of the person can be received via user input. For example, the body measurement can include neck size, arm length, chest size, waist size, leg length, and so on. The body measurement can be received using the communications interface 220 via the network 34.
For example, the list of parameters for men can include weight, height, chest, waist, and inseam, as later illustrated by the user inputs 1320 in FIG. 13. The list of parameters for women can include weight, height, bust, waist, and hips. Different female bodies can be generated based on the body parameters as illustrated in FIG. 15. Additionally, different bodies can also be created by interpolating between two bodies of specific measurements. The list of parameters is just representative, and is not intended to be exhaustive. Similarly, in some instances, the list can include a subset of the parameters listed.
At operation 430, a processor (e.g., the CPU 222) configured by the garment simulation module 246 can generate a three-dimensional body model based on the accessed body measurement from operation 420.
Once the body measurement has been accessed at operation 420, the system can create a set of 3-D human-like body models (e.g., static, animated, dynamic) for the content stage (e.g., fashion performance, 360° view, fit map, suggest a size).
In various embodiments, the creation of one or more 3-D human-like body models can be used to simulate a fashion runway experience. By using salient body parameters, the system 100 can create human-like body models to span the whole range of human bodies that can potentially wear a given garment. For example, the total number of human-like male bodies can be Nm, and the total number of human-like female bodies can be Nw.
At operation 440, the garment simulation module 246 can position at least a portion of the generated three-dimensional body model inside the garment points. In some instances, positioning can include placing the garment on or around the body, given that the body may be fixed in some embodiments. In these instances, the garment can be stretch and deformed based on the simulation. The garment simulation module 246 can configure at least one processor among the one or more processors (e.g., the CPU 222) to position the body model inside the garment model.
As previously mentioned, the garment model can consist of a set of shapes (e.g., triangles) to form the surface of the garment. The shapes can be created using lines connecting the vertices. Additionally the garment model can include physical properties associated with the lines (e.g., edges) and vertices in the mesh.
By simulating the garment model on each male and female body model, the application 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 described at operation 450. 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.
At operation 450, the garment simulation module 246 can calculate one or more simulated forces acting on a subset of the garment points based on the positioning of the generated three-dimensional body model inside the garment points. The garment simulation module 246 can configure at least one processor among the one or more processors (e.g., the CPU 222) to calculate the simulated force.
In some arrangements, the simulated force can include a gravitational force, an elastic force, a friction force, or an aerodynamic force. Additionally, the garment simulation module can further calculate the one or more simulated forces acting on the subset of the garment points based on the material property of the garment. For example, the simulated one or more forces can include a gravitational force and an elastic force, and the material property of the garment indicates a degree to which the garment is elastic. The material property of the garment can include, but is not limited to, a sheerness value, a linear stiffness value, or a bending stiffness value.
Operation 450 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 with operation 450 for a three-spring implementation of a sample triangle 550 with three vertices (i.e., a vertex 552, a vertex 554, a vertex 556) associated with a tessellated garment 540, as illustrated in FIG. 5.
$\begin{matrix} {spring}_{force_{1}} = (\frac{k_{s}}{{restlength}_{1}}) * (\langle x_{2} - x_{1} \rangle - {restlength}_{1}) * {spring}_{direction_{1}} + (\frac{k_{d}}{{restlength}_{1}}) * {Dot}_{product} (v_{2} - v_{1}, {spring}_{{direction}_{1}}) * {spring}_{{direction}_{1}} & (Equation 1) \\ {spring}_{force_{2}} = (\frac{k_{s}}{{restlength}_{2}}) * (\langle x 3 - x 2 \rangle - {restlength}_{2}) * {spring}_{direction_{2}} + (\frac{k_{d}}{{restlength}_{2}}) * {Dot}_{Product} (v 3 - v 2, {spring}_{{direction}_{2}}) * {spring}_{{direction}_{2}} & (Equation 2) \\ {spring}_{force_{3}} = (\frac{k_{s}}{{restlength}_{3}}) * (\langle x 1 - x 3 \rangle - {restlength}_{3}) * {spring}_{direction_{3}} + (\frac{k_{d}}{{restlength}_{3}}) * {Dot}_{Product} (v 1 - v 3, {spring}_{{direction}_{3}}) * {spring}_{{direction}_{3}} & (Equation 3) \end{matrix}$
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 simulation module 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 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, 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 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 resdength value, which can be determined by x₂−x₁(e.g., the current length of the spring) minus the resdength 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_sis 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/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−v₁in FIG. 5). 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_dis the damping spring constant to scale the damping force up/down, which can be multiplied by the spring direction to give the force a direction.
In various example embodiments, an individual simulation can be run for each displayed to a user. In some instances, for each of the bodies, the system 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 system 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., and V₂), the resultant force on V₁(and correspondingly V₂) equals:
F(V ₁)=k(V ₁ ,V ₂)*Delta(V ₁ −V ₂), (Equation 4)
where

- k(V₁,V₂) is the spring constant of the spring joining V₁and V₂(e.g., a function of the material property of the garment); and
- Delta(V₁−V₂) is a velocity-dependent force function based on the change in position vectors for V₁,V₂as compared to their original rest state. These forces can then be then accumulated for each vertex to compute the resultant force.

In various example embodiments, for each of the bodies, the system 100 can store the resultant force on each vertex. 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. For example, FIGS. 8-9 show the forces (tight regions depicted using warm colors, loose regions depicted using cool colors). As previously mentioned, for the mesh with K vertices, a total of 3K numbers are stored.
Now referring back to the method 400 of FIG. 4, at operation 460, the rendering module 248 can generate an image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more simulated forces. The rendering module 248 can configure at least one processor among the one or more processors (e.g., the CPU 222) to generate the image at operation 460. For example, the rendering module 248 can generate an image of the tessellated 3-D garment model on a 3-D 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 various example embodiments, operations 450 and 460 involve taking data from all previous operations, combining them, and inputting them into a cloth simulation engine. Additionally, the simulation results from operation 450 can be stored in the simulation result geometry files 258.
At operation 470, the display module 250 can present the generated image on a display of a device. 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.
For example, as illustrated in FIG. 6, using the two images, the garment creation module 246 can generate a first partial shape corresponding to the front of a pair of jeans 610 and a second partial shape corresponding to the back of the jeans 620. Then, the digital garment creation module can determine that the received images are images of a pair of jeans by comparing the generated partial shapes to the jeans garment template in the garment template database 255. Moreover, based on the determination that the garment is a pair of jeans, the digital garment creation module can join the partial shapes to generate a 3-D pair of digital jeans 630. As will be further described herein, the 3-D pair of digital jeans 630 can be tessellated. Furthermore, the access module 244 can receive the tessellated garment model at operation 410 of FIG. 4. Moreover, the 3-D pair of digital jeans 630 can be presented on an avatar 640 at operation 470 of FIG. 4. The avatar 640 can have similar dimensions to the user who is interested in purchasing the jeans. Optionally, a fit map 650 corresponding to the tightness or looseness of the jeans on the avatar 640 can be presented to the user.
In another example, as illustrated in FIG. 7, 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 dress 730 can be presented on an avatar 740 at operation 470. Additionally, the avatar 740 can illustrate how the dress looks and feels by demonstrating a fashion presentation 750 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 garments in use in everyday activities as later illustrated in FIG. 11.
The garment model can be draped on the body model. For example, the garment simulation module 246 can present the tessellated 3-D garment model on a 3-D body model using the draping module 265 and the simulation module 266. The tessellated 3-D garment model is presented based on the simulated force. The presentation can be done by draping the tessellated 3-D garment model onto a body model. In some embodiments, the garment simulation module 246 can put the digital garment onto a standard body, as illustrated by avatars 640 and 740.
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 it 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, the garment model can be draped on the body model. According to some example embodiments, the method 400 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 generated image at operation 470 with a generated fit map 810 as illustrated in FIG. 8.
According to another arrangement, 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. 8 shows an example of a fit map with color display cues. As illustrated in FIG. 8, the display cues can be overlaid on the rendered garment itself. As illustrated, the generated fit map 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 can label that section of the garment as a tight section 820. Alternatively, a loose section 830 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. 9, a fit map 910 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 910, each vertex of the shape (e.g., triangle) is assigned a red-green-blue (RGB) value. In some instances, the generated tit map 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 920, while loose sections of the garment can be colored blue 930. 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).
In various example embodiments, for both of the above arrangements, 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 use cases, such as videos, 3-D objects, or text description of the simulation output.
Optionally, texture and optical properties can be determined from the information accessed at operation 410 and 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 fit map. The material properties of the garment can be used for calculating the simulated forces on the garment model at operation 450. Furthermore, the material properties can be matched to the garment template database 255 in order to determine the type of garment using the texture mapping module 262.
According to another embodiment, the rendering module 248 can be configured to distort the three-dimensional garment model, and the display module 250 can present the distorted three-dimensional garment model. For example, the distorted three-dimensional garment model can be presented at operation 470 using the display module 250. The rendering module 248 can distort the three-dimensional garment model by stretching or twisting the three-dimensional 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.
Additionally, as illustrated in FIG. 10, 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. 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. The specifications can be based on grading rules, size charts, actual measurements, or digital measurements. In the example illustrated in FIG. 10, the garment is distorted based on the stiffness of the shirt, where the leftmost shirt 1010 is the stiffest and the rightmost shirt 1020 has the least amount of stiffness.
FIG. 11 illustrates how a garment looks and feels by demonstrating a lifestyle presentation using the method 400, according to some example embodiments. The body model described in the method 400 can have a first body position 1110, and the garment simulation module 246 is further configured to change the three-dimensional body model to a second body position 1120. By animating the body model (e.g., a user swinging a golf club, a model walking down a runway), the method 400 can be configured to generate a three-dimensional digital media content simulation. In some instances, changing the body positions of the body model can present an animation the body model. After the body model is changed to the second body position 1120, the garment simulation module 246 can reposition at least a portion of the three-dimensional body model inside the garment points based on the change of the three-dimensional body model to the second body position 1120. Furthermore, after the repositioning, the garment simulation module 246 can calculate the simulated forces acting on a second subset of the garment points based on the repositioning.
Additionally, the rendering module 248 is further configured to animate the generated image as the three-dimensional body model moves from the first body position 1110 to the second body position 1120, and subsequently to a third body position 1130, which can be presented using the display module 250.
The system can animate each of the body meshes under different animation sequences, such as swinging a golf club, as illustrated in FIG. 11. 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 system is set at 30 frames-per-second animation, the system can compute 300 frames (10 seconds times 30 frames) of the body.
In various example embodiments, for each of the above animation frames, the system 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. The system 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. FIG. 12 shows a sequence of animation frames on a female avatar performing a fashion presentation.
Moreover, the precision can be adjusted to accommodate varying levels of desired accuracy of the garment model and can be based on computation power. The precision can be automatically adjusted by the system 100 based on the client device (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.
Furthermore, the material properties can be matched to the garment template database 255 in order to determine the type of garment using the texture mapping module 262. For example, the system 100 can identify pleats in a garment based on the information accessed at operation 410. Additionally, the material property can be extracted even if the images of the garment are stretched or sheared.
In some instances, the draping parameters files 256 can extracted from the garment template database 255. Additionally, the simulation parameters files 257 can also extracted from the garment template database 255.
Techniques for suggesting a recommended size from the given set of sizes for a garment are provided, in accordance with example embodiments. As previously mentioned, 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, XXL). By computing the tension map for each size for the user's avatar, a recommended size can be suggested, as illustrated in FIG. 13. The recommended size can be based on the size that fits the avatar's dimensions the closest with minimum distortion to the garment.
According to some example embodiments, the garment simulation module 246 can be further configured to determine a size from a set of sizes for the garment based on the calculated simulated forces or the generated fit map. For example, using the generated fit map, the garment simulation module 246 can determine the recommended size for a pair of jeans. Accordingly, the display module 250 can present a determined size 1310, such as a size 10 for this example, to a user. Furthermore, the garment simulation module 246 can determine a recommended size based on the available garment sizes stored in the file system 242. For example, the garment simulation module 246 can determine the recommended size based on a database of reference garment shapes using the garment template database 255 and the stitching module 264.
In some instances, the body measurements of the user can be user inputs 1320. FIG. 14 presents a user interface for inputting body measurements (e.g., waist 1410, weight 1420, chest 1430, height 1440). Subsequently, the garment simulation module 246 can generate different three-dimensional body models based on the body measurements as illustrated in FIG. 15.
In other instances, the body measurements of a user can be received from photographs 1330 using a calibration object 1340. Calibration can assign an x, y, z position value to each pixel. If the garment is laid out on a planar surface, the system 100 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 1340, the system 100 can extract the four corner points, and given the dimensions of the calibration object 1340, the system 100 has enough information to compute the calibration. Based on the calibration, the system 100 can present the garment on an avatar 1350 and display properties 1360 (e.g., rise measurement, inseam measurement, hips measurement, thigh measurement, calf measurement) associated with the garment. Similarly, with a grid paper as a calibration object, the system can use the relative positions of three points to compute this calibration. Additionally, the body model can be generated based on purchase history and feedback. Feedback can include returns and acceptances of purchases.
As previously mentioned, based on the accessed body measurements from operation 410, the garment simulation module 246 can generate different three-dimensional body models as illustrated in FIG. 15. Since different users have different dimensions, the body models (e.g., body model 1510, body model 1520) can be specifically tailored to the user in order to accurately show how an article of clothing fits.
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 system 100 can infer that the garment has a tight fit. Alternatively, the system 100 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 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. 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)) for each garment category can be built based on the dimensions that either fit or do not fit the user. For a given new garment in a specific category, the system 100 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. 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).
As illustrated in FIG. 16, the simulation module 266 can take as input tessellation and material properties and can output 3-D models of clothing on avatars 640 and 740. The simulation module 266 can use digitization 1610, modeling 1620, simulation 1630, and automated 1640 techniques to generate a three-dimensional simulation. The simulation module 266 can move 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 simulation module 266 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. As illustrated in FIG. 17, some example embodiments described herein can generate a 3-D body model of a customer 1710 based on operations 420 and 430 of FIG. 4. Additionally, information corresponding to a 3-D garment for sale 1720 can be accessed at operation 410. Subsequently, a presentation 1730 can drape the 3-D garment for sale 1720 on the 3-D body model of a customer 1710 based on operations 440, 450, 460 and 470.
Moreover, one or more of the methodologies described herein may facilitate the visualization of different styles of a garment on a 3-D body model using the garment simulation module 246. For example, FIG. 18 illustrates how a customer can visualize the look and feel of different pairs of khakis. In this example, the customer, using the fit map (not pictured), can visualize that the signature khaki 1810 is a looser fit, in comparison to the alpha khaki. Additionally, the customer can visualize how the fire-brush-colored alpha khaki 1820 and the new-british-colored alpha khaki 1830 look in relation to the customer's own skin tone.
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 digitizing 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. 19 is a block diagram illustrating components of a machine 1900, according to some example embodiments, able to read instructions 1924 from a machine-readable medium 1922 (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. 19 shows the machine 1900 in the example form of a computer system (e.g., a computer) within which the instructions 1924 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1900 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 1900.
In alternative embodiments, the machine 1900 operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 1900 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 1900 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 1924, 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 1924 to perform all or part of any one or more of the methodologies discussed herein.
The machine 1900 includes a processor 1902 (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 (MC), or any suitable combination thereof), a main memory 1904, and a static memory 1906, which are configured to communicate with each other via a bus 1908. The processor 1902 may contain microcircuits that are configurable, temporarily or permanently, by some or all of the instructions 1924 such that the processor 1902 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 1902 may be configurable to execute one or more modules (e.g., software modules) described herein.
The machine 1900 may further include a graphics display 1910 (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 1900 may also include an alphanumeric input device 1912 (e.g., a keyboard or keypad), a cursor control device 1914 (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, an eye tracking device, or other pointing instrument), a storage unit 1916, an audio generation device 1918 (e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), and a network interface device 1920.
The storage unit 1916 includes the machine-readable medium 1922 (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions 1924 embodying any one or more of the methodologies or functions described herein. The instructions 1924 may also reside, completely or at least partially, within the main memory 1904, within the processor 1902 (e.g., within the processor's cache memory), or both, before or during execution thereof by the machine 1900. Accordingly, the main memory 1904 and the processor 1902 may be considered machine-readable media tangible and non-transitory machine-readable media). The instructions 1924 may be transmitted or received over the network 34 via the network interface device 1920. For example, the network interface device 1920 may communicate the instructions 1924 using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)).
The machine-readable medium 1922 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 another instruction format that is interpreted by one or more processors.
In some example embodiments, the machine 1900 may be a portable computing device, such as a smartphone or tablet computer, and have one or more additional input components 1930 (e.g., sensors or gauges). Examples of such input components 1930 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 1922 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 1924. 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 1924 for execution by the machine 1900, such that the instructions 1924, when executed by one or more processors of the machine 1900 (e.g., the processor 1902), cause the machine 1900 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

What is claimed is:

1. A system comprising:

an access module configured to:

access, from a database, a three-dimensional garment model of a garment, the three-dimensional garment model including garment points that represent a surface of the garment; and

access a body measurement of a person;

a processor configured by a garment simulation module to:

generate a three-dimensional body model based on the body measurement;

position at least a portion of the generated three-dimensional body model inside the garment points; and

calculate one or more simulated forces acting on a subset of the garment points based on the positioning of the generated three-dimensional body model inside the garment points;

a rendering module configured to generate an image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more simulated forces; and

a display module configured to cause presentation of the generated image on a display of a device.

2. The system of claim 1, wherein the one or more simulated forces include a gravitational force.

3. The system of claim 1, wherein the garment simulation module is further configured to calculate the one or more simulated forces acting on the subset of the garment points based on a material property of the garment.

4. The system of claim 3, wherein the one or more simulated forces include an elastic force and the material property of the garment indicates a degree to which the garment is elastic.

5. The system of claim 3, wherein the material property of the garment includes a sheerness value.

6. The system of claim 3, wherein the material property of the garment includes a linear stiffness value.

7. The system of claim 3, wherein the material property of the garment includes a bending stiffness value.

8. The system of claim 1, wherein the one or more simulated forces include a friction force.

9. The system of claim 1, wherein the garment simulation module is further configured to determine a size from a set of sizes for the garment based on the calculated one or more simulated forces, and wherein the display module is further configured to present the determined size on the display of the device.

10. The system of claim 1, wherein the garment simulation module is further configured to generate a fit map based on the calculated one or more simulated forces, and wherein the display module is configured to present the generated image with the generated fit map.

11. The system of claim 10, wherein the generated fit map is colored based on a magnitude of the calculated one or more simulated forces.

12. The system of claim 1, wherein the one or more simulated forces include an aerodynamic force.

13. The system of claim 1, wherein the three-dimensional body model has a first body position, and wherein the garment simulation module is further configured to:

change the three-dimensional body model to a second body position;

reposition at least a portion of the three-dimensional body model inside the garment points based on the change of the three-dimensional body model to the second body position; and

calculate the one or more simulated forces acting on a second subset of the garment points based on the repositioning.

14. The system of claim 13, wherein the rendering module is further configured to animate the generated image as the three-dimensional body model moves from the first body position to the second body position, and wherein the display module is further configured to cause presentation of the animation.

15. The system of claim 1, wherein the rendering module is further configured to distort the three-dimensional garment model, and wherein the display module is father configured to cause presentation of an image of the distorted three-dimensional garment model.

16. The system of claim 15, wherein the rendering module distorts the three-dimensional garment model by stretching or twisting the three-dimensional garment model.

17. A method comprising:

accessing, from a database, a three-dimensional garment model of a garment, the three-dimensional garment model including garment points that represent a surface of the garment;

accessing a body measurement of a person;

generating, using a processor, a three-dimensional body model based on the body measurement;

positioning at least a portion of the generated three-dimensional body model inside the garment points;

calculating one or more simulated forces acting on a subset of the garment points based on the positioning of the generated three-dimensional body model inside the garment points;

generating a first image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more simulated forces; and

presenting the generated image on a display of a device.

18. The method of claim 17, further comprising:

generating a fit map based on the calculated one or more simulated forces; and

presenting the generated image with the generated fit map.

19. The method of claim 17, wherein the three-dimensional body model has a first body position and the generated image is a generated first image, the method further comprising:

changing the three-dimensional body model to a second body position;

repositioning at least a portion of the three-dimensional body model inside the garment points based on the change of the three-dimensional body model to the second body position;

calculating one or more second simulated forces acting on a second subset of the garment points based on the repositioning;

generating a second image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more second simulated forces; and

animating the three-dimensional garment model draped on the three-dimensional body model by transitioning from the generated first image to the generated second image.

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:

accessing a three-dimensional garment model of a garment, the three-dimensional garment model including garment points that represent a surface of the garment;

accessing a body measurement of a person;

generating a three-dimensional body model based on the body measurement;

generating an image of the three-dimensional garment model draped on the three-dimensional body model based on the calculated one or more simulated forces; and

presenting e generated image on a display of a device.