KR102494222B1 - Method for creating 3d eyebrows automatically - Google Patents

Abstract

The present disclosure relates to a method for generating 3D eyebrows from an input image including a face, which is performed by at least one computing device for solving the above problems. The method may include the steps of: displaying a 2D eyebrow region on an input image; generating a texture map for 3D conversion based on a modified image in which the 2D eyebrow region is displayed; extracting a 3D eyebrow region based on the 2D eyebrow region expressed in the texture map; and generating a 3D eyebrow based on the 3D eyebrow region.

Description

How to automatically create 3D eyebrows {METHOD FOR CREATING 3D EYEBROWS AUTOMATICALLY}

The present disclosure relates to a method of generating 3D eyebrows, and more particularly, to a method of automatically generating 3D eyebrows related to computer graphics based on an input image including a face.

Today, in various video, game, AR and VR fields, there is a demand for computer graphics (ie, digital humans) that mimic real people.

In the conventional method of generating a digital human, detailed regions such as hair included in eyebrows are expressed as a single texture for efficiency, but there is a problem in that naturalness is poor because it is different from the eyebrows of a real person. However, when the hairs included in the eyebrows are manually expressed in order to naturally express the eyebrows of a digital human, there is a difficulty in that considerable human resources are consumed. Therefore, a need for a method of automatically generating 3D eyebrows, rather than manually, when creating a digital human or 3D eyebrows has recently emerged in the field.

On the other hand, the present disclosure has been derived at least based on the technical background salpin above, but the technical problem or object of the present disclosure is not limited to solving the above salpin problems or disadvantages. That is, the present disclosure may cover various technical issues related to the content to be described below, in addition to the technical issues discussed above.

The present disclosure aims to automatically generate 3D eyebrows based on an input image. For example, an object of the present disclosure is to generate 3D eyebrows by displaying an eyebrow area in an input image and 3D converting the input image in which the eyebrow area is displayed.

On the other hand, the technical problem to be achieved by the present disclosure is not limited to the above-mentioned technical problem, and may include various technical problems within a range apparent to those skilled in the art from the contents to be described below.

A method for generating 3D eyebrows from an input image including a face, which is performed by at least one computing device for solving the above object, the method comprising: displaying a 2D eyebrow region on the input image; Generating a texture map for 3D conversion based on the modified image in which the eyebrow region is displayed; extracting a 3D eyebrow region based on the 2D eyebrow region expressed in the texture map; and and generating 3D eyebrows based on the 3D eyebrow region.

Alternatively, generating a texture map for 3D conversion based on the corrected image in which the 2D eyebrow region is displayed includes a 3D facial structure and texture based on a face included in the corrected image. It may include generating a map.

Alternatively, extracting the 3D eyebrow region based on the 2D eyebrow region expressed in the texture map may include generating a 3D face by applying a texture map in which the 2D eyebrow region is expressed to the 3D face structure. , and generating a 3D eyebrow region based on the 2D eyebrow region expressed on the 3D face and the 3D facial structure.

Alternatively, generating the 3D eyebrows based on the 3D eyebrow region may include generating a plurality of guide curves inside the 3D eyebrow region.

Alternatively, generating the 3D eyebrow based on the 3D eyebrow region may include selecting a 3D eyebrow sample corresponding to the 3D eyebrow region in a 3D eyebrow sample database using a neural network model, and the 3D eyebrow sample corresponding to the 3D eyebrow region. and generating 3D eyebrows based on the eyebrow region and the selected 3D eyebrow sample.

Alternatively, the generating of the 3D eyebrows based on the 3D eyebrow region may include generating a plurality of guide curves within the 3D eyebrow region, using a neural network model, in the 3D eyebrow sample database. Selecting 3D eyebrow samples corresponding to guide curves, and generating 3D eyebrows based on the 3D eyebrow region, the plurality of guide curves, and the selected 3D eyebrow sample.

Alternatively, the method may further include adjusting properties of the 3D eyebrows.

Alternatively, the properties of the 3D eyebrows may include at least one of eyebrow thickness, hair root thickness, shadow density, eyebrow density, eyebrow color, or eyebrow flexibility.

Alternatively, the displaying of the 2D eyebrow region in the input image may include identifying a 2D eyebrow region included in the input image using a region classification model, and including the identified 2D eyebrow region in the input image. It may include a display step.

An apparatus for solving the above object, comprising at least one processor and a memory, wherein the at least one processor displays a 2D eyebrow area in an input image, and based on a modified image in which the 2D eyebrow area is displayed, , generate a texture map for 3D conversion, extract a 3D eyebrow region based on the 2D eyebrow region expressed in the texture map, and generate a 3D eyebrow region based on the 3D eyebrow region.

A computer program stored in a computer readable storage medium for solving the above problems, the program causing a computing device to perform operations for generating 3D eyebrows from an input image including a face, the operations comprising: Displaying the 2D eyebrow region on the 2D eyebrow region, generating a texture map for 3D conversion based on the modified image in which the 2D eyebrow region is displayed, and based on the 2D eyebrow region expressed in the texture map, the 3D eyebrow region An operation of extracting , and an operation of generating 3D eyebrows based on the 3D eyebrow region may be included.

According to the present disclosure, 3D eyebrows can be generated based on an input image without human intervention. For example, according to the present disclosure, a 2D eyebrow region may be automatically displayed on an input image, a 3D eyebrow region may be generated based on the 2D eyebrow region, and a 3D eyebrow region may be generated based on the 3D eyebrow region.

On the other hand, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents to be described below.

1 is a block diagram of a computing device performing operations according to an embodiment of the present disclosure.
2 is a schematic diagram illustrating a neural network model according to an embodiment of the present disclosure.
3 is a flowchart illustrating a method of generating 3D eyebrows according to an embodiment of the present disclosure.
4 is a schematic diagram illustrating a method of generating 3D eyebrows according to an embodiment of the present disclosure.
5 is a schematic diagram illustrating a method of generating 3D eyebrows using a neural network model based on 3D eyebrows, according to an embodiment of the present disclosure.
6 is a schematic diagram illustrating a method of generating 3D eyebrows using a neural network model based on 3D eyebrows and a guide curve, according to an embodiment of the present disclosure.
7 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this disclosure, various descriptions are presented to provide an understanding of the disclosure. However, it is apparent that these embodiments may be practiced without these specific details.

The terms “component,” “module,” “system,” and the like, as used in this disclosure, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term “and/or” as used in this disclosure should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in the disclosure and claims should generally be construed to mean "one or more".

In addition, the term “at least one of A or B” should be interpreted as meaning “when only A is included”, “when only B is included” and “when A and B are combined”.

Skilled artisans will further understand that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or both. It should be recognized that it can be implemented with combinations of To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not limited to the embodiments presented herein. This disclosure is to be interpreted in the widest light consistent with the principles and novel features presented herein.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

The processor 110 may include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit), data analysis, and processors for deep learning. The processor 110 may read a computer program stored in the memory 130 and process data for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network model. The processor 110 processes input data for learning in deep learning (DL), extracts features from input data, calculates an error, and learns a neural network model such as weight update of the neural network model using backpropagation. Calculations can be performed for At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the neural network model. For example, the CPU and GPGPU can process learning of neural network models and data classification using neural network models. In addition, in an embodiment of the present disclosure, the neural network model learning and data classification using the neural network model may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The above description of the memory is only an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( Various wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) may be used.

In addition, the network unit 150 presented in the present disclosure includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), SC-FDMA ( Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). can In addition, the network may be the known World Wide Web (WWW), or may use a wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. The techniques described in this disclosure may also be used in other networks mentioned above.

2 is a schematic diagram showing a neural network according to an embodiment of the present disclosure.

Throughout this disclosure, neural network and neural network may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight (at this time, parameters and weights may be used in the same meaning throughout the present disclosure). The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may constitute one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from that described above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

A neural network model including a neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network model may be a process of applying knowledge for the neural network model to perform a specific operation to the neural network model.

The neural network model can be trained in a way that minimizes errors in output. In the learning of the neural network model, the learning data is repeatedly input into the neural network model, the output of the neural network model for the training data and the error of the target are calculated, and the error of the neural network model is transferred from the output layer of the neural network model to the input layer in the direction of reducing the error. This is the process of updating the weight of each node of the neural network model by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network model, and an error may be calculated by comparing an output (category) of the neural network model with a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network model output. The calculated error is back-propagated in the neural network model in the reverse direction (ie, from the output layer to the input layer), and the connection weight of each node in each layer of the neural network model may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The computation of the neural network model on the input data and the backpropagation of errors may constitute an epoch. A learning rate may be applied differently according to the number of repetitions of epochs of the neural network model. For example, a high learning rate is used in the early stages of neural network model training so that the neural network model can quickly achieve a certain level of performance to increase efficiency, and a low learning rate can be used in the late stage to increase accuracy.

In learning neural network models, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network model), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be increasing epochs. Over-fitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network model that learns a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing training data, regularization, inactivating some nodes in the network during learning, dropout, and batch normalization can be applied. .

According to an embodiment of the present disclosure, a computer readable medium storing a data structure is disclosed.

Data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. Data structure may refer to the organization of data to solve a specific problem (eg, data retrieval, data storage, data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements may include a connection relationship between user-defined data elements. A physical relationship between data elements may include an actual relationship between data elements physically stored in a computer-readable storage medium (eg, a persistent storage device). The data structure may specifically include a set of data, a relationship between data, and a function or command applicable to the data. Through an effectively designed data structure, a computing device can perform calculations while using minimal resources of the computing device. Specifically, the computing device can increase the efficiency of operation, reading, insertion, deletion, comparison, exchange, and search through an effectively designed data structure.

The data structure can be divided into a linear data structure and a non-linear data structure according to the shape of the data structure. A linear data structure may be a structure in which only one data is connected after one data. Linear data structures may include lists, stacks, queues, and decks. A list may refer to a series of data sets in which order exists internally. The list may include a linked list. A linked list may be a data structure in which data are connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer can contain information about connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on the form. A stack can be a data enumeration structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a LIFO-Last in First Out (Last in First Out) data structure. A queue is a data listing structure that allows limited access to data, and unlike a stack, it can be a data structure (FIFO-First in First Out) in which data stored later comes out later. A deck can be a data structure that can handle data from either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data are connected after one data. The non-linear data structure may include a graph data structure. A graph data structure can be defined as a vertex and an edge, and an edge can include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in a graph data structure.

The data structure may include a neural network model. And the data structure including the neural network model may be stored in a computer readable medium. The data structure including the neural network model includes preprocessed data for processing by the neural network model, data input to the neural network model, weights of the neural network model, hyperparameters of the neural network model, data obtained from the neural network model, and each node or layer of the neural network model. It may include an activation function related to , a loss function for learning a neural network model, and the like. A data structure including a neural network model may include any of the components disclosed above. That is, the data structure including the neural network model includes preprocessed data for processing by the neural network model, data input to the neural network model, weights of the neural network model, hyperparameters of the neural network model, data obtained from the neural network model, each node of the neural network model, or It may be configured to include all or any combination of an activation function associated with a layer, a loss function for learning a neural network model, and the like. In addition to the foregoing configurations, the data structure containing the neural network model may include any other information that determines the characteristics of the neural network model. In addition, the data structure may include all types of data used or generated in the calculation process of the neural network model, but is not limited to the above. A computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium. A neural network model may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network model includes one or more nodes.

The data structure may include data input to the neural network model. A data structure including data input to the neural network model may be stored in a computer readable medium. Data input to the neural network model may include training data input during the learning process of the neural network model and/or input data input to the neural network model after learning has been completed. Data input to the neural network model may include pre-processed data and/or data to be pre-processed. Preprocessing may include a data processing process for inputting data to a neural network model. Accordingly, the data structure may include data subject to pre-processing and data generated by pre-processing. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network model. (In this disclosure, weights and parameters may be used in the same meaning.) Also, a data structure including weights of a neural network model may be stored in a computer readable medium. A neural network model may include a plurality of weights. The weight may be variable, and may be changed by a user or an algorithm in order to perform a desired function of the neural network model. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. A data value output from an output node may be determined based on the weight. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

As a non-limiting example, the weights may include weights that are varied during the learning process of the neural network model and/or weights for which the neural network model has been trained. The variable weight in the learning process of the neural network model may include a weight at a time point at which an epoch starts and/or a weight variable during an epoch. The weights for which the learning of the neural network model has been completed may include weights for which epochs have been completed. Therefore, the data structure including the weights of the neural network model may include a data structure including weights that are variable during the learning process of the neural network model and/or weights for which the neural network model has been trained. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network model. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure including the weights of the neural network model may be stored in a computer readable storage medium (eg, a memory or a hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or another computing device and later reconstructed and used. The computing device may transmit and receive data through a network by serializing the data structure. A data structure including weights of a serialized neural network model may be reconstructed on the same computing device or another computing device through deserialization. The data structure including the weights of the neural network model is not limited to serialization. Furthermore, the data structure including the weights of the neural network model is a data structure for increasing the efficiency of operation while minimizing the resource of the computing device (for example, B-Tree, Trie, m-way search tree, AVL tree in nonlinear data structure) , Red-Black Tree). The foregoing is only an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network model. Also, the data structure including the hyperparameters of the neural network model may be stored in a computer readable medium. A hyperparameter may be a variable variable by a user. Hyperparameters include, for example, learning rate, cost function, number of epoch iterations, weight initialization (eg, setting the range of weight values to be targeted for weight initialization), It may include the number of Hidden Units (eg, the number of hidden layers and the number of nodes in the hidden layer). The foregoing data structure is only an example, and the present disclosure is not limited thereto.

Prior to describing one embodiment of the present disclosure, terms used throughout the present disclosure are explained.

'Digital human' used in the first half of this disclosure may refer to computer graphics used in various video, game, AR, and VR fields and whose appearance corresponds to a real person. However, the digital human is an example, not a limitation, and can be used in various fields. Also, the '2D eyebrow region' may mean a region that can be displayed on a 2D image that can be semantically classified as an eyebrow. For example, the 2D eyebrow area may include the inside of a 2D figure created by straight lines converging on a curve and connected to a plurality of vertices indicating the outer edges of the eyebrows. Also, the '3D eyebrow area', like the '2D eyebrow area', semantically classified as an eyebrow, may mean an area that can be displayed in 3D. For example, when it is assumed that there is a digital human including a face, the 3D eyebrow area may include a surface of a face part that semantically corresponds to the eyebrows of the 3D graphic model. In this case, the surface may include a curved surface. Also, a '3D face' can be created using a 3D face structure, texture map and UV map. In this case, the 3D face structure may correspond to 3D data representing the geometrical shape of the face, and the texture map may correspond to 2D data including texture, color, contrast, and boundary of the face. In addition, the UV map may correspond to a development view showing how the texture map is applied to the face structure. Finally, the 3D eyebrows may mean a set of a plurality of hairs included in the 3D eyebrow area described above. In this case, the hair may be generated by copying the shape of a guide curve included in the 3D eyebrow area. For example, the processor 110 generates at least one hair that imitates the shape of the guide curve included in the 3D eyebrow area based on the guide curve, but the detailed properties such as density, flexibility, thickness, and color of the hair are generated. can include However, the above example is an example, not a limitation, and 3D eyebrows may be generated by various methods other than the method of the above example.

Now, referring to steps S300 to S303 of FIG. 3 , an overview process of a method for generating 3D eyebrows based on an input image according to an embodiment of the present disclosure will be described.

Referring to FIG. 3 , in order to generate 3D eyebrows from an input image including a face, the processor 110 displays a 2D eyebrow region on the input image (S300), and converts the modified image in which the 2D eyebrow region is displayed Generating a texture map for 3D conversion as a basis (S301) Extracting a 3D eyebrow region based on the 2D eyebrow region expressed in the texture map (S302) and 3D eyebrows based on the 3D eyebrow region The step of generating (S303) can be performed.

At this time, in relation to the step of displaying the 2D region on the input image (S300), the processor 110 may identify the 2D eyebrow region included in the input image by using a region classification model, which is a kind of neural network model. . For example, the processor 110 may classify regions such as eyes, nose, mouth, and eyebrows by using a region classification model based on an input image including a face. As an additional example, the processor 110 selects a plurality of landmark points that are characteristics of the face by using a region classification model based on an input image including a face, and selects landmark points related to the eyebrows and their surroundings. It can be classified as a 2D eyebrow area corresponding to . Subsequently, the processor 110 may display the identified 2D eyebrow area on the input image. For example, the 2D eyebrow region classified based on the aforementioned region classification model may be displayed in the input image as a chromakey color. In this case, any color for clearly distinguishing the 2D eyebrow region from the surrounding environment may be used as the chroma key color. In addition, the method of displaying the 2D eyebrow area in the input image may be implemented in various ways other than expressing it in a specific color including a chroma key color.

In addition, in relation to the step of generating a texture map for 3D conversion (S301) based on the corrected image in which the 2D eyebrow region is displayed, the processor 110 generates a texture map based on the face included in the corrected image. In addition to maps, 3D facial structures and UV maps can be created. For example, the processor 110 may generate a texture map, a 3D face structure, and a UV map corresponding to the input image from an input image using a 3DMM or GAN-based neural network model related to DECA.

In addition, in relation to the step of extracting a 3D eyebrow region based on the 2D eyebrow region expressed in the texture map (S302), the processor 110 extracts a texture map in which the 2D eyebrow region is expressed in the 3D face structure A 3D face may be generated by applying the 3D face, and a 3D eyebrow region may be generated based on the 2D eyebrow region expressed on the 3D face and the 3D face structure.

Meanwhile, the processor 110 may additionally perform a step of adjusting the properties of the 3D eyebrows following step S302. In this case, the adjustable 3D eyebrow property may include at least one of eyebrow thickness, hair root thickness, shadow density, eyebrow density, eyebrow color, or eyebrow flexibility. However, the properties of the adjustable 3D eyebrows are not limited thereto, and various properties related to the eyebrows may be further included. For example, the properties of the 3D eyebrows may be set based on a computer program for 3D graphic modeling including 'MAYA Xgen'.

In short, in step S300, the processor 110 "displays a 2D eyebrow region on the input image manually or by using the region classification model" → in step S301, "displays a neural network model based on the modified image in which the 2D eyebrow region is displayed." 3D facial structure, texture map and UV map for 3D transformation are generated using " → In step S302, "The 2D eyebrow region expressed in the texture map is applied to the 3D facial structure to create a 3D eyebrow region Extraction" → In step S303, it is possible to "generate 3D eyebrows based on the 3D eyebrow region". At this time, in an effective aspect, the processor 110 using the method can generate 3D eyebrows composed of hairs to which a physics engine is applied from a 2D image, and the 2D image to be the input image is a real person or digital human. Since photos can be used, 3D eyebrows that imitate the eyebrows of the existing person can be created, or hairs to which a physical engine is applied based on the eyebrows of a digital human composed of textures (eg, a capture image of a digital human) It is possible to create configured 3D eyebrows.

4 is a schematic diagram illustrating a method of generating 3D eyebrows according to an embodiment of the present disclosure.

Previously, in the general process of the present disclosure, it has been described that the processor 110 may generate 3D eyebrows based on an input image by performing steps S300 to S303. Referring to FIG. 4 , an embodiment related to steps S300 to S303 performed by a processor is disclosed.

An embodiment of the present disclosure performed by the processor 110 may be largely classified into four modules. The four modules are: ① '2D eyebrow area generation module' that displays the 2D eyebrow area 410 based on the input image 400, ② 3D facial structure based on the input image with the 2D eyebrow area 410 displayed ( 420) and a '3D face generation module' for generating a 3D face to which a texture map is generated and applied, ③ a '3D eyebrow area extraction module' for extracting a 3D eyebrow area 440 based on the 3D face 430 on which the eyebrow area is displayed. ' And, ④ '3D eyebrow generation module' for generating 3D eyebrows 460 based on the 3D eyebrow region 440 may be included.

At this time, in relation to ① the '2D eyebrow region generation module', the processor 110 uses a region classification model (eg, face segmentation model) based on the input image 400 including the face to provide eyes, nose, and mouth. , eyebrows, etc. can be classified. As an additional example, the processor 110 selects a plurality of landmark points that are characteristics of the face by using a region classification model based on an input image including a face, and selects landmark points related to the eyebrows and their surroundings. It can be classified as a 2D eyebrow region 410 corresponding to . Subsequently, the processor 110 may display the identified 2D eyebrow area 410 on the input image. For example, the 2D eyebrow region 410 classified based on the aforementioned region classification model may be displayed in the input image as a chromakey color. In this case, any color for clearly distinguishing the 2D eyebrow region 410 from the surrounding environment may be used as the chroma key color. In addition, the method of displaying the 2D eyebrow area 410 in the input image may be implemented in various ways other than displaying it in a specific color including a chroma key color.

Also, in connection with the ② '3D face generation module', the processor 110 may generate a 3D face structure 420 and a UV map as well as a texture map based on the face included in the modified image. . For example, the processor 110 may generate a texture map, a 3D facial structure 420, and a UV map corresponding to the input image from an input image using a 3DMM or GAN-based neural network model related to DECA. Subsequently, the processor 110 may generate the 3D face 430 on which the 2D eyebrow region 410 is displayed based on the texture map on which the 2D eyebrow region 410 is displayed, the 3D face structure 420, and the UV map. .

Also, in connection with ③ '3D eyebrow region extraction module', the processor 110 may extract the 3D eyebrow region 440 from the 3D face 430 on which the 2D eyebrow region 410 is displayed. For example, the processor 110 may extract only the surface corresponding to the 2D eyebrow region 410 included in the 3D face 430 and correspond to the 3D eyebrow region 440 . For example, the processor 110 deletes or fills the surface other than the 2D eyebrow area 410 based on the 3D face 430 with a first color, and the surface corresponding to the 2D eyebrow area 410 as the first color. It can be filled with a second color different from the color. As an additional example, the 3D eyebrow area 440 may be created by removing a surface corresponding to the 2D eyebrow area 410 from the 3D face 430 to create a new object.

In addition, in relation to ④ '3D eyebrow generation module', the processor 110 generates a plurality of guide curves 450 inside the 3D eyebrow region 440, and based on the guide curves 450 3D eyebrows 460 may be created. At this time, the guide curve 450 is generated by filling the 3D eyebrow area 440 with the guide curve 450 of a predetermined shape by the processor 110, or the processor 110 uses a neural network model to create an appropriate shape. A method of selecting 3D eyebrow samples may be used. At this time, an embodiment using a neural network model will be described in detail later with reference to FIGS. 5 and 6 .

5 is a schematic diagram illustrating a method of generating 3D eyebrows using a neural network model based on 3D eyebrows, according to an embodiment of the present disclosure.

Previously, it has been mentioned that in relation to ④ '3D eyebrow generation module', the processor 110 may use a method of selecting a 3D eyebrow sample of an appropriate shape using a neural network model. As one of them, referring to FIG. 5 , an embodiment in which the processor 110 selects a 3D eyebrow sample and generates a 3D eyebrow by using a neural network model based on an eyebrow region will be described.

Referring to FIG. 5 , the processor 110 may select a 3D eyebrow sample corresponding to the 3D eyebrow region 510 in the 3D eyebrow sample database 500 using a neural network model 520, and A 3D eyebrow 530 may be generated based on the eyebrow region and the selected 3D eyebrow sample. For example, the processor 110 compares the shape and position of the eyebrow region 510 with the shape and position of the eyebrow samples included in the 3D eyebrow sample database 500 based on the neural network model 520, and obtains the most consistent degree. A 3D eyebrow sample with a high value can be selected. Subsequently, the processor 110 may generate the 3D eyebrow 530 by filling the 3D eyebrow region 510 based on the selected attribute of the 3D eyebrow sample and the guide curve. At this time, each of the 3D eyebrow samples included in the eyebrow sample database 500 may include eyebrow attributes, 3D eyebrow regions, and guide curves. In addition, the attribute may include at least one of eyebrow thickness, hair root thickness, shadow density, eyebrow density, eyebrow color, or eyebrow flexibility. In terms of effectiveness, when generating 3D eyebrows 530 that can be naturally used for a person or digital human included in an input image, a user's decision or manual work is not required, so it is possible to create 3D eyebrows 530 more efficiently than manual work. can be created, and human error can be prevented.

6 is a schematic diagram illustrating a method of generating 3D eyebrows using a neural network model based on 3D eyebrows and a guide curve, according to an embodiment of the present disclosure.

④ Regarding the '3D eyebrow generation module', the processor 110 selects an eyebrow sample from the 3D eyebrow sample database 600 using a neural network model based on the eyebrow region 620 and the guide curve 610, , an embodiment of generating 3D eyebrows is described.

Earlier, it was mentioned that "an embodiment of using a neural network model based on a region of a 3D eyebrow can efficiently create a 3D eyebrow compared to a manual operation" to generate 3D eyebrows. In addition to this, the eyebrow sample may be selected by additionally considering the guide curve 610 related to the input image as well as the 3D eyebrow region 620 related to the input image. That is, not only the 3D eyebrow region 620 but also the guide curve 610 may be additionally considered as parameters considered by the neural network model 630 to select the 3D eyebrow sample. For example, the processor 110 generates a plurality of guide curves 610 in the 3D eyebrow region 620 based on the eyebrow shape of the input image, and uses the neural network model 630 to generate a 3D eyebrow sample database Selecting a 3D eyebrow sample corresponding to the plurality of guide curves 610 in (600), and the 3D eyebrow region 620, the plurality of guide curves 610 and the selected 3D eyebrow sample A step of generating the 3D eyebrows 640 based on the base may be performed. At this time, as a difference from the previous embodiment, the attribute included in the selected 3D eyebrow sample is considered, but the guide curve 610 is selected in the "step of generating a plurality of guide curves 610 inside the 3D eyebrow region". You can use the generated guide curve. Also, in relation to the step of generating the plurality of guide curves 610 within the 3D eyebrow area 620, the guide curves may be created by referring to the shape of the eyebrows of the input image. At this time, in terms of effectiveness, compared to the previous embodiment, it is possible to generate 3D eyebrows 640 that can be more naturally applied to the person or digital human of the input image. In addition, when using an embodiment in which the processor 110 generates the 3D eyebrows 640, the direction of generation of the 3D eyebrows 640 that the user wants to direct may be reflected.

7 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above as being generally embodied by a computing device, those skilled in the art will understand that the present disclosure may be combined with computer executable instructions and/or other program modules that may be executed on one or more computers and/or with hardware. It will be appreciated that it can be implemented as a combination of software.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, it will be appreciated by those skilled in the art that the methods of the present disclosure may be used in single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like. It will be appreciated that other computer system configurations may be implemented, including (each of which may be operative in connection with one or more associated devices).

The described embodiments of the present disclosure may be practiced in a distributed computing environment where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Computer readable media can be any medium that can be accessed by a computer, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer readable transmission media.

An exemplary environment 1100 implementing various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106 , to processing unit 1104 . Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

System bus 1108 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and is a basic set of information that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may include high-speed RAM, such as static RAM for caching data.

Computer 1102 includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA), which internal hard disk drive 1114 may be configured for external use in a suitable chassis (not shown). , a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM disk ( 1122) or for reading from or writing to other high-capacity optical media such as DVD). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to the system bus 1108 by a hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, and cartridges. It will be appreciated that other tangible computer readable media such as , , and the like may also be used in the exemplary operating environment and that any such media may include computer executable instructions for performing the methods of the present disclosure. .

A number of program modules may be stored on the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, handheld computer, microprocessor-based entertainment device, peer device, or other common network node, and generally includes It includes many or all of the components described for, but for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 1102 connects to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communications to LAN 1152 , which includes a wireless access point installed therein to communicate with wireless adapter 1156 . When used in a WAN networking environment, computer 1102 may include a modem 1158, be connected to a communicating computing device on WAN 1154, or establish communications over WAN 1154, such as over the Internet. have other means. A modem 1158, which may be internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored on remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1102 is any wireless device or entity that is deployed and operating in wireless communication, eg, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags associated with It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet without wires. Wi-Fi is a wireless technology, such as a cell phone, that allows such devices, eg, computers, to transmit and receive data both indoors and outdoors, i.e. anywhere within coverage of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band) .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields s or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (11)

A method of generating 3D eyebrows from an input image including a face, performed by at least one computing device, the method comprising:
generating a corrected image by displaying a 2D eyebrow region on the input image;
generating a texture map for extracting a 3D eyebrow region based on the modified image in which the 2D eyebrow region is displayed;
extracting the 3D eyebrow region based on the 2D eyebrow region expressed in the texture map; and
generating 3D eyebrows based on the 3D eyebrow region;
including,
Generating 3D eyebrows based on the 3D eyebrow region,
generating a plurality of guide curves inside the 3D eyebrow region; and
generating the 3D eyebrow based on the 3D eyebrow area and the plurality of guide curves;
including,
method.
According to claim 1,
The step of generating a texture map for extracting the 3D eyebrow region based on the modified image in which the 2D eyebrow region is displayed,
generating a 3D face structure and the texture map based on a face included in the modified image;
including,
method.
According to claim 2,
The step of extracting the 3D eyebrow region based on the 2D eyebrow region expressed in the texture map,
generating a 3D face by applying a texture map representing the 2D eyebrow region to the 3D face structure; and
generating the 3D eyebrow area based on the 2D eyebrow area and the 3D facial structure expressed in the 3D face;
including,
method.
delete According to claim 3,
Generating 3D eyebrows based on the 3D eyebrow region,
selecting a 3D eyebrow sample corresponding to the 3D eyebrow region in a 3D eyebrow sample database using a neural network model; and
generating 3D eyebrows based on the 3D eyebrow region and the selected 3D eyebrow sample;
containing
method.
According to claim 3,
Generating 3D eyebrows based on the 3D eyebrow region,
selecting a 3D eyebrow sample corresponding to the plurality of guide curves in a 3D eyebrow sample database using a neural network model; and
generating a 3D eyebrow based on the 3D eyebrow region, the plurality of guide curves, and the selected 3D eyebrow sample;
containing
method.
According to claim 1,
The method,
Adjusting the properties of the 3D eyebrows
Including more,
method.
According to claim 7,
The properties of the 3D eyebrows,
Eyebrow thickness, hair root thickness, shadow density, eyebrow density, eyebrow color or eyebrow flexibility
including at least one of
method.
According to claim 1,
The step of displaying the 2D eyebrow area on the input image,
identifying a 2D eyebrow region included in the input image using a region classification model; and
displaying the identified 2D eyebrow region on the input image;
including,
method.
As a device,
at least one processor; and
Memory;
including,
The at least one processor,
Create a modified image by displaying the 2D eyebrow region on the input image,
Based on the modified image in which the 2D eyebrow region is displayed, a texture map for extracting a 3D eyebrow region is generated;
Extracting the 3D eyebrow region based on the 2D eyebrow region expressed in the texture map, and
configured to generate 3D eyebrows based on the 3D eyebrow region;
Generating 3D eyebrows based on the 3D eyebrow area,
creating a plurality of guide curves inside the 3D eyebrow area; and
Generating the 3D eyebrow based on the 3D eyebrow region and the plurality of guide curves,
Device.
A computer program stored on a computer readable storage medium, the program causing a computing device to perform operations for generating 3D eyebrows from an input image comprising a face, the operations comprising:
generating a corrected image by displaying a 2D eyebrow region on the input image;
generating a texture map for extracting a 3D eyebrow region based on the modified image in which the 2D eyebrow region is displayed;
extracting the 3D eyebrow region based on the 2D eyebrow region expressed in the texture map; and
generating 3D eyebrows based on the 3D eyebrow area;
including,
The operation of generating 3D eyebrows based on the 3D eyebrow region,
generating a plurality of guide curves inside the 3D eyebrow region; and
generating the 3D eyebrow based on the 3D eyebrow area and the plurality of guide curves;
including,
A computer program stored on a computer readable storage medium.

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