Material image generation system, material image generation method, and program

Abstract

This material image generation system comprises: a request information acquisition unit that acquires request information indicating a feature of a design requested by a user; and an image generation unit that, on the basis of a correspondence relationship between a first material image indicating a design of a material and a feature of the design of the material indicated by the first material image, generates a second material image indicating a design matching the user's request indicated by the request information.

Description

Material image generation system, material image generation method, and program

[0001] This application claims priority to Japanese Patent Application No. 2024-052249, filed on March 27, 2024, the contents of which are incorporated herein by reference.

[0002] Traditionally, the process of creating designs for building materials and other items involves a business receiving requests from a customer, creating a design, proposing the design to the customer, and then receiving evaluation from the customer. This process is usually repeated multiple times, incurring high costs for both the business and the customer until a design is agreed upon.

[0003] In this regard, various technologies capable of automatically generating designs have been proposed. For example, Patent Document 1 listed below discloses a technology in which a computer automatically generates a wood grain pattern that gives an impression closer to that of natural wood, based on the pattern that appears on the cut surface when a three-dimensional tree model is cut along a predetermined plane.

[0004] Japanese Patent No. 3966919

[0005] However, the technology described in Patent Document 1 does not allow customers' requests to be reflected in the generated design, so businesses cannot obtain designs that satisfy their customers' requests. Therefore, although this technology can automate the creation of designs, it cannot shorten the time required for the process of deciding on a design, and therefore cannot reduce costs.

[0006] In view of the above-mentioned problems, an object of the present invention is to provide a material image generation system, a material image generation method, and a program that can reduce the cost of creating designs.

[0007] In order to solve the above-mentioned problems, a material image generation system according to one embodiment of the present invention comprises a request information acquisition unit that acquires request information indicating the design features desired by a user, and an image generation unit that generates a second material image that indicates a design that meets the user's request indicated by the request information based on the correspondence between a first material image that indicates the design of the material and the design features of the material indicated by the first material image.

[0008] A material image generation method according to one aspect of the present invention is executed by a computer and includes a request information acquisition process for acquiring request information indicating the design features desired by a user, and an image generation process for generating a second material image indicating a design that meets the user's request indicated by the request information based on the correspondence between a first material image indicating the design of the material and the design features of the material indicated by the first material image.

[0009] A program according to one aspect of the present invention is a program for causing a computer to function as a request information acquisition means for acquiring request information indicating the design features desired by a user, and an image generation means for generating a second material image showing a design that meets the user's request indicated by the request information based on the correspondence between a first material image showing the design of the material and the design features of the material indicated by the first material image.

[0010] A computer-readable non-transitory storage medium (storage medium) storing the program of the present invention causes a computer to acquire request information indicating the design features desired by a user, and generate a second material image showing a design that meets the user's request indicated by the request information based on the correspondence between a first material image showing the design of the material and the design features of the material indicated by the first material image.

[0011] According to the present invention, the cost of producing a design can be reduced.

[0012] FIG. 1 is a diagram showing an overview of a building material design output service according to the present embodiment; FIG. 2 is a block diagram showing an example of the configuration of a material image generation system according to the present embodiment; FIG. 3 is a block diagram showing an example of the functional configuration of a design learning device according to the present embodiment; FIG. 4 is a block diagram showing an example of the functional configuration of a material image generation device according to the present embodiment; FIG. 5 is a flowchart showing an example of the flow of a design learning process according to the present embodiment; FIG. 6 is a flowchart showing an example of the flow of a material image generation process according to the present embodiment.

[0013] An embodiment of the present invention will be described in detail below with reference to the drawings. In this embodiment, a material image generation system is described that generates an image showing a material design (hereinafter also referred to as a "material image"). Examples of materials include wood grain, stone, and fabric. The following describes this embodiment by taking as an example an example a case in which the material image generation system is applied to a building material design output service. The building material design output service is a service in which a business dealing in building materials provides designs of building materials to users (customers). Examples of building materials include flooring, decorative paper, wallpaper, and interior decorative materials. Decorative sheets are used for surface decoration of building materials. In response to a user's request, the material image generation system generates a material image showing a design applicable to the design of the building material and outputs the material image to the user. The user can apply the generated material design to the building material by printing the material image output from the material image generation system on a decorative sheet and attaching the decorative sheet to the surface of the building material.

[0014] <1. Overview of building material design output service> An overview of the building material design output service according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an overview of the building material design output service according to this embodiment.

[0015] In the building material design output service SA shown in FIG. 1 , a material image generation model MD that has been trained on generating building material designs based on a training dataset DS is prepared in advance (step S0). The training dataset DS is data in which label information is assigned to a plurality of material images MG1 (first material images) prepared for training. For example, the material image MG1-1 is labeled with the design features of the material shown in the material image MG1-1 as label information. Furthermore, the material image MG1-2, which shows a design different from that of the material image MG1-1, is labeled with the design features of the material shown in the material image MG1-2 as label information. The design features that are labeled as label information include, for example, color, surface treatment, and the presence or absence of knots.

[0016] A user using the building material design output service SA inputs their design requirements (step S1). The user can input information indicating the design features desired by the user (hereinafter also referred to as "request information"), for example, by text, labels, images, sketches, etc. In the case of text input, the user verbalizes the desired design features and inputs them in text. In the case of label input, the user selects a label indicating the desired design features. In the case of image input, the user prepares and inputs an image indicating the desired design features. In the case of sketch input, the user prepares and inputs a sketch indicating the desired design features.

[0017] Based on the request information input by the user, the material image generation model MD generates and outputs a material image MG2 (second material image) showing a design that meets the user's request indicated by the request information (step S2). The material image generation model MD generates and outputs an image showing wood grain, for example, like the material image MG2 shown in FIG.

[0018] The user evaluates the design of the output material image MG2 (step S3). If the evaluation results in agreement with the output design (step S4), the user incorporates the design of the material image MG2 into a product design (step S5).

[0019] The material image MG2 evaluated by the user is an image generated in accordance with the user's wishes and has a design that is highly appealing to the user. Therefore, the design is easily agreed upon. This reduces the frequency with which the material image MG2 is regenerated when agreement on the design cannot be reached. On the other hand, if agreement on the design cannot be reached, the user can easily regenerate a material image MG2 with a design that more closely matches the user's wishes by adjusting the wishes they input.

[0020] 2. Configuration of the Material Image Generation System The building material design output service SA according to this embodiment has been described above. Next, the configuration of the material image generation system according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the material image generation system according to this embodiment. The material image generation system 1 shown in Fig. 2 is a system for operating the building material design output service SA outlined with reference to Fig. 1.

[0021] 2, the material image generation system 1 includes an administrator terminal 10, a user terminal 20, a design learning device 30, and a material image generation device 40. The network NW may be configured to transmit and receive information using, for example, a local area network (LAN), a wide area network (WAN), a telephone network (such as a mobile phone network or a landline telephone network), a regional Internet Protocol (IP) network, or the Internet.

[0022] (1) Administrator Terminal 10 The administrator terminal 10 is a terminal operated by an administrator (business operator) to manage the building material design output service SA. The administrator terminal 10 is, for example, a smartphone, a tablet terminal, a PC (Personal Computer), etc. The administrator terminal 10 is communicably connected to the design learning device 30 and the material image generation device 40 via the network NW.

[0023] In communication with the design learning device 30, the administrator terminal 10 transmits a plurality of material images MG1 prepared for learning, label information, etc., and receives a material image generation model MD. The administrator operates the administrator terminal 10 to transmit the material images MG1 for learning and label information to the design learning device 30.

[0024] The training material image MG1 is, for example, image data obtained by scanning pattern manuscript data prepared for building material design. The label information is information that is labeled for the training material image MG1 based on the perspective of applying the material design to the design of the building material. The label information is prepared, for example, by a designer with experience in building material design, who extracts design features from each material image MG1. The material image generation model MD is a model generated by the design learning device 30 based on the training material image MG1 and label information transmitted from the administrator terminal 10.

[0025] In communication with the material image generation device 40, the administrator terminal 10 transmits a material image generation model MD. The material image generation model MD is a model that the administrator terminal 10 received from the design learning device 30.

[0026] On the administrator terminal 10, various UIs (User Interfaces) are displayed by an application (hereinafter also referred to as an "administrator app") that allows the administrator to manage the building material design output service SA. The administrator can manage the building material design output service SA by operating the UI displayed on the administrator terminal 10 by the administrator app. Note that the functions of the administrator app may be provided by installing the administrator app on the administrator terminal 10 (i.e., a native app), or may be provided by a web system (i.e., a web app). In the case of a web app, the administrator app is managed by a server, and its functions are provided via a web browser.

[0027] (2) User Terminal 20 The user terminal 20 is a terminal that a user operates to use the building material design output service SA. The user terminal 20 is, for example, a smartphone, a tablet terminal, a PC, etc. The user terminal 20 is communicably connected to the material image generation device 40 via the network NW.

[0028] In communication with the material image generation device 40, the user terminal 20 transmits desired information and receives a material image MG2. The user operates the user terminal 20 to input desired information indicating desired design features and transmits the information to the material image generation device 40. The material image MG2 is an image generated by the material image generation device 40 based on the desired information transmitted from the user terminal 20.

[0029] Various UIs are displayed on the user terminal 20 by an application (hereinafter also referred to as a "user app") that allows the user to use the building material design output service SA. The user can use the building material design output service SA by operating the UI displayed on the user terminal 20 by the user app. Note that the functions of the user app may be provided by installing the user app on the user terminal 20 (i.e., a native app), or may be provided by a web system (i.e., a web app). In the case of a web app, the user app is managed by a server, and its functions are provided via a web browser.

[0030] (3) Design Learning Device 30 The design learning device 30 is a device that generates a material image generation model MD. The design learning device 30 is configured, for example, by one or more PCs or server devices (e.g., cloud servers). The design learning device 30 is connected to the administrator terminal 10 via the network NW so as to be able to communicate with the administrator terminal 10.

[0031] In communication with the administrator terminal 10, the design learning device 30 receives a plurality of material images MG1 prepared for learning, label information, etc., and transmits a material image generation model MD. The design learning device 30 creates a learning dataset DS based on the learning material images MG1 and label information received from the administrator terminal 10, and generates a material image generation model MD using the learning dataset DS.

[0032] (4) Material Image Generating Device 40 The material image generating device 40 is a device that generates a material image MG2 with a design desired by a user. The material image generating device 40 is configured, for example, by one or more PCs or server devices (e.g., cloud servers). The material image generating device 40 is communicably connected to the administrator terminal 10 and the user terminal 20 via the network NW.

[0033] The material image generating device 40 receives the material image generation model MD in communication with the administrator terminal 10. The material image generating device 40 generates a material image MG2 using the material image generation model MD received from the administrator terminal 10.

[0034] In communication with the user terminal 20, the material image generation device 40 receives request information and transmits a material image MG2. The material image generation device 40 generates the material image MG2 by inputting the request information received from the user terminal 20 into the material image generation model MD.

[0035] 3. Functional Configuration of Design Learning Device 30 The configuration of the material image generation system 1 according to this embodiment has been described above. Next, the functional configuration of the design learning device 30 according to this embodiment will be described with reference to FIG. 3. FIG. 3 is a block diagram showing an example of the functional configuration of the design learning device 30 according to this embodiment. As shown in FIG. 3, the design learning device 30 includes a communication unit 310, a storage unit 320, and a control unit 330.

[0036] (1) Communication Unit 310 The communication unit 310 has the function of transmitting and receiving various information. The communication unit 310 is communicably connected to the administrator terminal 10 via the network NW, and transmits and receives various information. In communication with the administrator terminal 10, the communication unit 310 receives a plurality of material images MG1 prepared for learning, label information, etc., and transmits a material image generation model MD.

[0037] (2) Storage Unit 320 The storage unit 320 has a function of storing various information. The storage unit 320 is configured by a storage medium provided as hardware in the design learning device 30, such as a hard disk drive (HDD), a solid state drive (SSD), a flash memory, an electrically erasable programmable read-only memory (EEPROM), a random access read / write memory (RAM), a read-only memory (ROM), or any combination of these storage media. As shown in FIG. 3 , the storage unit 320 includes a material image storage unit 321, a learning dataset storage unit 322, and a material image generation model storage unit 323.

[0038] (2-1) Material Image Storage Unit 321 The material image storage unit 321 has a function of storing material images. The material image storage unit 321 stores, for example, learning material images MG1 that are prepared in advance by the administrator and received by the communication unit 310 from the administrator terminal 10. The material image storage unit 321 stores multiple material images MG1 with different combinations of material and design features.

[0039] (2-2) Training Dataset Storage Unit 322 The training dataset storage unit 322 has a function of storing a training dataset DS. The training dataset storage unit 322 stores, for example, a training dataset DS created by the data processing unit 332 described below. The training dataset storage unit 322 stores, as a training dataset DS, a plurality of data labeled with label information for each material image MG1 stored in the material image storage unit 321.

[0040] (2-3) Material Image Generation Model Storage Unit 323 The material image generation model storage unit 323 has the function of storing material image generation models MD. As shown in FIG. 3, the material image generation model storage unit 323 stores a reference model MD1 (first trained model) and an additional model MD2 (second trained model) as material image generation models MD. Both the reference model MD1 and the additional model MD2 are trained models that have learned the correspondence between a training material image MG1 and the design features of the material indicated by the training material image MG1. However, the reference model MD1 and the additional model MD2 use different training datasets DS for training.

[0041] The reference model MD1 is a model that has learned about correspondences between design features of materials shown in the training material image MG1 using a training dataset DS in which the training material image MG1 is labeled as label information. In other words, the reference model MD1 is a model that has learned design features evenly using all of the prepared training datasets DS.

[0042] The additional model MD2 is a model that learns about correspondences between design features of the material indicated by the material image MG1 using a training dataset DS labeled with specific features from a training dataset DS in which the design features of the material indicated by the material image MG1 are labeled as label information for the material image MG1. In other words, the additional model MD2 is a model that learns by focusing on specific design features using a portion of the prepared training datasets DS. The additional model MD2 is realized, for example, by LoRA (Low-Rank Adaptation).

[0043] (3) Control Unit 330 The control unit 330 has a function of controlling the overall operation of the design learning device 30. The control unit 330 is realized, for example, by causing a central processing unit (CPU) or a graphics processing unit (GPU) provided as hardware in the design learning device 30 to execute a program. As shown in FIG. 3 , the control unit 330 includes a data acquisition unit 331, a data processing unit 332, a learning unit 333, and an output processing unit 334.

[0044] (3-1) Data Acquisition Unit 331 The data acquisition unit 331 has a function of acquiring various data. For example, the data acquisition unit 331 acquires the learning material image MG1 and label information received by the communication unit 310 from the administrator terminal 10.

[0045] (3-2) Data Processing Unit 332 The data processing unit 332 has a function of performing various data processing. For example, the data processing unit 332 creates a training dataset DS based on the training material image MG1 and label information acquired by the data acquisition unit 331.

[0046] (3-3) Learning Unit 333 The learning unit 333 has a function of generating a trained model through machine learning. For example, the learning unit 333 uses the training dataset DS created by the data processing unit 332 to learn the correspondence between the training material images MG1 and design features. Through this learning, the learning unit 333 generates a material image generation model MD that, when request information is input, can generate and output a material image that meets the user's request indicated by the request information.

[0047] When generating the reference model MD1, the learning unit 333 learns the correspondence between the training material image MG1 and the design features, for example, using all the training datasets DS stored in the training dataset memory unit 322.

[0048] When generating the additional model MD2, the learning unit 333 uses only the training datasets DS stored in the training dataset storage unit 322, for example, that are labeled with specific features as label information, to learn the correspondence between the training material image MG1 and the design features. As an example, the learning unit 333 extracts, from all the training datasets DS, a training dataset DS in which "knots" is labeled as a design feature for the training material image MG1. When learning using this extracted training dataset DS, the learning unit 333 can generate an additional model MD2 that is more likely to output a material image MG2 with a knotted design. In this way, the learning unit 333 generates and prepares an additional model MD2 for each of multiple specific features.

[0049] The learning unit 333 uses the high-resolution learning material image MG1 without reducing it to generate a material image generation model MD (reference model MD1 and additional model MD2) that has learned the correspondence relationship. This allows the material image generation model MD to generate and output a high-resolution material image MG2.

[0050] (3-4) Output Processing Unit 334 The output processing unit 334 has a function of performing processing related to the output of various information. For example, the output processing unit 334 transmits the material image generation model MD generated by the learning unit 333 from the communication unit 310 to the administrator terminal 10.

[0051] 4. Functional Configuration of Material Image Generating Device 40 The functional configuration of the design learning device 30 according to this embodiment has been described above. Next, the functional configuration of the material image generating device 40 according to this embodiment will be described with reference to FIG. 4. FIG. 4 is a block diagram showing an example of the functional configuration of the material image generating device 40 according to this embodiment. As shown in FIG. 4, the material image generating device 40 includes a communication unit 410, a storage unit 420, and a control unit 430.

[0052] (1) Communication Unit 410 The communication unit 410 has the function of transmitting and receiving various information. The communication unit 410 is communicably connected to the administrator terminal 10 and the user terminal 20 via the network NW, and transmits and receives various information to and from each terminal. In communication with the administrator terminal 10, the communication unit 410 receives a material image generation model MD. In communication with the user terminal 20, the communication unit 410 receives request information and transmits a material image MG2.

[0053] (2) Storage Unit 420 The storage unit 420 has a function of storing various types of information. The storage unit 420 is configured by a storage medium provided as hardware in the material image generation device 40, such as an HDD, SSD, flash memory, EEPROM, RAM, ROM, or any combination of these storage media. As shown in FIG. 4 , the storage unit 420 includes a material image generation model storage unit 421 and a material image storage unit 422.

[0054] (2-1) Material Image Generation Model Storage Unit 421 The material image generation model storage unit 421 has a function of storing material image generation models MD. As shown in Fig. 4, the material image generation model storage unit 421 stores a reference model MD1 and an additional model MD2 as material image generation models MD. The reference model MD1 and the additional model MD2 are models generated by the design learning device 30 and received by the communication unit 410 from the administrator terminal 10.

[0055] (2-2) Material Image Storage Unit 422 The material image storage unit 422 has a function of storing material images. The material image storage unit 422 stores, for example, a material image MG2 generated by an image generation unit 432 (described later).

[0056] (3) Control Unit 430 The control unit 430 has a function of controlling the overall operation of the material image generation device 40. The control unit 430 is realized, for example, by causing a CPU or GPU provided as hardware in the material image generation device 40 to execute a program. As shown in FIG. 4 , the control unit 430 includes a request information acquisition unit 431, an image generation unit 432, a model output control unit 433, and an output processing unit 434.

[0057] (3-1) Request Information Acquisition Unit 431 The request information acquisition unit 431 has a function of acquiring request information. The request information acquisition unit 431 acquires the request information that the communication unit 410 receives from the user terminal 20.

[0058] (3-2) Image Generation Unit 432 The image generation unit 432 has a function of generating a material image MG2. Based on the correspondence between the training material image MG1 and the design features of the material indicated by the training material image MG1, the image generation unit 432 generates a material image MG2 that shows a design that meets the user's needs indicated by the request information acquisition unit 431. The image generation unit 432 generates the material image MG2 by inputting the request information into the reference model MD1 stored in the material image generation model storage unit 421. In this way, by using the request information, the image generation unit 432 can generate a material image MG2 with a high level of design that meets the user's image.

[0059] The image generation unit 432 generates the material image MG2 using the reference model MD1, which has been learned about the correspondence using the high-resolution training material image MG1 without reducing it, thereby enabling the image generation unit 432 to output the high-resolution material image MG2.

[0060] (3-3) Model Output Control Unit 433 The model output control unit 433 has the function of controlling the output of the material image generation model MD. The model output control unit 433 controls the output of the reference model MD1 using an additional model MD2 that has learned correspondences for specific features among the design features of the material indicated by the training material image MG1. For example, if the request information acquired by the request information acquisition unit 431 includes a specific feature, the model output control unit 433 controls the reference model MD1 to generate and output a material image MG2 with a design that is closer to the specific feature, using the additional model MD2. This allows the model output control unit 433 to more accurately output a material image MG2 that matches the user's image.

[0061] (3-4) Output Processing Unit 434 The output processing unit 434 has a function of performing processing related to the output of various information. For example, the output processing unit 434 transmits the material image MG2 generated by the image generation unit 432 from the communication unit 410 to the user terminal 20 and displays it on the user terminal 20.

[0062] 5. Processing Flow The functional configuration of the material image generating device 40 according to this embodiment has been described above. Next, the processing flow according to this embodiment will be described with reference to FIGS.

[0063] (1) Design Learning Processing The flow of the design learning processing according to this embodiment will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of the flow of the design learning processing according to this embodiment.

[0064] 5, first, the data acquisition unit 331 of the design learning device 30 acquires the learning material image MG1 (step S101). Specifically, the data acquisition unit 331 acquires the learning material image MG1 that is sent by the administrator from the administrator terminal 10 to the design learning device 30 and received by the communication unit 310.

[0065] The data acquisition unit 331 also acquires label information corresponding to the acquired learning material image MG1 (step S102). Specifically, the data acquisition unit 331 acquires the label information that is sent by the administrator from the administrator terminal 10 to the design learning device 30 and received by the communication unit 310.

[0066] Next, the data processing unit 332 of the design learning device 30 creates a learning dataset DS (step S103) based on the learning material images MG1 and the label information acquired by the data acquisition unit 331. Specifically, the data processing unit 332 creates the learning dataset DS by labeling each of the learning material images MG1 with corresponding label information.

[0067] Next, the learning unit 333 of the design learning device 30 performs learning using the learning dataset DS created by the data processing unit 332 (step S104). Specifically, the learning unit 333 uses all of the learning datasets DS to learn the correspondence between the learning material images MG1 and the design features. Through this learning, the learning unit 333 generates a reference model MD1 (step S105). The output processing unit 334 transmits the reference model MD1 created by the learning unit 333 to the material image generation device 40 via the administrator terminal 10.

[0068] Next, the learning unit 333 extracts a training dataset DS (step S106). Specifically, the learning unit 333 extracts, from all the training datasets DS, a training dataset DS in which a specific feature designated by, for example, an administrator is labeled as label information.

[0069] Next, the learning unit 333 performs learning using the extracted learning dataset DS (step S107). Specifically, the learning unit 333 uses only the extracted learning dataset DS to learn the correspondence between the learning material image MG1 and the design features. Through this learning, the learning unit 333 generates an additional model MD2 (step S108). The output processing unit 334 transmits the additional model MD2 generated by the learning unit 333 to the material image generation device 40 via the administrator terminal 10.

[0070] (2) Material Image Generation Processing The flow of the material image generation processing according to this embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the flow of the material image generation processing according to this embodiment.

[0071] 6, first, the request information acquisition unit 431 of the material image generation device 40 acquires the request information (step S201). Specifically, the request information acquisition unit 431 acquires the request information that is input by the user to the user terminal 20, transmitted from the user terminal 20 to the material image generation device 40, and received by the communication unit 410.

[0072] Next, the model output control unit 433 of the material image generating device 40 checks whether or not a specific feature is included in the user's request indicated by the request information acquisition unit 431 (step S202). If the specific feature is included (step S202 / YES), the process proceeds to step S203. On the other hand, if the specific feature is not included (step S202 / NO), the process proceeds to step S205.

[0073] If the process proceeds to step S203, the model output control unit 433 selects an additional model MD2 to be applied to the reference model MD1 (step S203). Specifically, the model output control unit 433 selects, from the prepared additional models MD2, an additional model MD2 that has learned specific features included in the user's request. The model output control unit 433 applies the selected additional model MD2 to the reference model MD1 (step S204). After application, the process proceeds to step S205.

[0074] If the process proceeds to step S205, the image generation unit 432 generates a material image MG2 (step S205). Specifically, the image generation unit 432 generates the material image MG2 by inputting the request information acquired by the request information acquisition unit 431 into the reference model MD1. If the user's request includes a specific feature, the image generation unit 432, under the control of the model output control unit 433, inputs the request information into the reference model MD1 to which the additional model MD2 has been applied, thereby generating a material image MG2 designed to reflect the specific feature.

[0075] Next, the output processing unit 434 of the material image generating device 40 transmits the material image MG2 generated by the image generating unit 432 from the communication unit 410 to the user terminal 20, and displays it on the user terminal 20 (step S206).

[0076] The processing flow according to this embodiment has been described above. As described above, the material image generation system 1 according to this embodiment includes a request information acquisition unit 431 that acquires request information indicating design features desired by a user, and an image generation unit 432 that generates a second material image that indicates a design that meets the user's request indicated by the request information, based on the correspondence between a first material image that indicates the design of the material and the design features of the material indicated by the first material image.

[0077] With the above configuration, the material image generation system 1 according to this embodiment can reflect the user's (customer's) wishes in the generated design. This allows businesses and customers to reduce the number of times they must go through the cycle of creation, proposal, and evaluation in the design production process, thereby shortening the time required for the design production process. Therefore, the material image generation system 1 according to this embodiment makes it possible to reduce the cost of design production.

[0078] Furthermore, the material image generation system 1 according to this embodiment generates a material image MG2 using a reference model MD1 that has been trained using a high-resolution training material image MG1 without reducing it, thereby outputting a high-resolution material image MG2, allowing the user to obtain a high-resolution material image MG2 that can withstand platemaking.

[0079] Furthermore, the material image generation system 1 according to this embodiment can generate a material image MG2 with a high level of design (highly sophisticated design) that matches the user's image by using the request information, which allows the user to utilize highly sophisticated decorative sheets not only for building materials but also for products that use decorative sheets for surface decoration.

[0080] Furthermore, the material image generation system 1 according to this embodiment achieves overwhelming efficiency in the design production process by shortening the time required for the design production process, and can also help expand the user's creativity and realize the ideal surface design or space. Furthermore, users can take advantage of the uniqueness of the output material images (high resolution, high design quality, etc.) and use the material images in digital content.

[0081] 6. Modifications The above describes the embodiments. Next, modifications of the above-described embodiments will be described. Note that each modification described below may be applied to the embodiment alone or in combination with the embodiment. Furthermore, each modification may be applied in place of the configuration described in the embodiment, or may be applied in addition to the configuration described in the embodiment.

[0082] In the above-described embodiment, an example in which the material image generation system is applied to a building material design output service has been described, but the application is not limited to the above-described example. For example, the material image generation system may be applied to a service provided in a virtual space (metaverse space).

[0083] In the above-described embodiment, an example has been described in which the design learning function and the material image generating function are respectively implemented by different devices (the design learning device 30 and the material image generating device 40), but this is not limited to the above example. For example, the design learning function and the material image generating function may be implemented by a single device having both functions.

[0084] In the above-described embodiment, an example has been described in which the learning material image MG1 and label information are transmitted from the administrator terminal 10 to the design learning device 30, and the learning dataset DS is created in the design learning device 30. However, the present invention is not limited to the above-described example. For example, the learning dataset DS may be created in advance by the administrator in the administrator terminal 10 and transmitted from the administrator terminal 10 to the design learning device 30.

[0085] In the above-described embodiment, an example has been described in which the material image generation model MD generated by the design learning device 30 is registered in the material image generation device 40 via the administrator terminal 10, but the present invention is not limited to the above-described example. For example, the design learning device 30 and the material image generation device 40 may be able to communicate with each other via the network NW, and the material image generation model MD may be sent directly from the design learning device 30 to the material image generation device 40.

[0086] The above describes a modified embodiment of the present invention. Note that some or all of the material image generation system 1, administrator terminal 10, user terminal 20, design learning device 30, and material image generation device 40 in the above-described embodiment may be implemented by a computer. In this case, a program for implementing these functions may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed. Note that the term "computer system" here includes hardware such as an OS and peripheral devices. Furthermore, the term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into a computer system. Furthermore, the term "computer-readable recording medium" may also include recording media that dynamically store programs for a short period of time, such as communication lines used when transmitting programs via networks such as the Internet or telephone lines, and recording media that store programs for a fixed period of time, such as volatile memory within the computer systems that serve as servers or clients in such cases. Furthermore, the program may be a program for realizing some of the above-described functions, or may be a program that can realize the above-described functions in combination with a program already recorded in a computer system, or may be a program that is realized using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0087] The embodiments of the present invention have been described in detail above with reference to the drawings, but the specific configuration is not limited to the above configuration, and various design changes and the like are possible within the scope that does not deviate from the gist of the present invention.

[0088] According to the present invention, the cost of producing a design can be reduced.

[0089] 1...Material image generation system, 10...Administrator terminal, 20...User terminal, 30...Design learning device, 40...Material image generation device, 310...Communication unit, 320...Memory unit, 321...Material image memory unit, 322...Learning dataset memory unit, 323...Material image generation model memory unit, 330...Control unit, 331...Data acquisition unit, 332...Data processing unit, 333...Learning unit, 334...Output processing unit, 410...Communication unit, 420...Memory unit, 421...Material image generation model memory unit, 422...Material image memory unit, 430...Control unit, 431...Request information acquisition unit, 432...Image generation unit, 433...Model output control unit, 434...Output processing unit, MD...Material image generation model, MD1...Reference model, MD2...Additional model, NW...Network, SA...Building material design output service

Claims

1. A material image generation system comprising: a request information acquisition unit that acquires request information indicating the design features desired by a user; and an image generation unit that generates a second material image that indicates a design that meets the user's request indicated by the request information based on the correspondence between a first material image that indicates the design of the material and the design features of the material indicated by the first material image.

2. The material image generation system of claim 1, wherein the image generation unit generates the second material image by inputting the desired information into a first trained model that has learned the correspondence between the first material image showing the design of the material and the design features of the material shown by the first material image.

3. The material image generation system of claim 2, wherein the first trained model is a model that has learned about the correspondence using a dataset in which the design features of the material indicated by the first material image are labeled as label information for the first material image.

4. The material image generation system of claim 2, further comprising a model output control unit that controls the output of the first trained model using a second trained model that has learned the correspondence relationship for specific features of the design of the material shown in the first material image.

5. The material image generation system described in claim 4, wherein, when the request information includes the specific feature, the model output control unit controls the first trained model to use the second trained model to generate and output the second material image having a design that is closer to the specific feature.

6. The material image generation system of claim 4, wherein the second trained model is a model that has learned about the correspondence using a dataset in which the specific features of the design of the material indicated by the first material image are labeled as label information for the first material image.

7. The material image generation system of claim 1, wherein the image generation unit uses the high-resolution first material image without reducing it, and generates the second material image using a trained model that has learned about the correspondence.

8. A material image generation system as described in claim 3 or claim 6, wherein the label information is information that is labeled for the first material image based on the perspective when applying the design of the material to the design of building materials.

9. A material image generation method executed by a computer, comprising: a request information acquisition process for acquiring request information indicating the design features desired by a user; and an image generation process for generating a second material image showing a design that meets the user's request indicated by the request information, based on the correspondence between a first material image showing the design of the material and the design features of the material indicated by the first material image.

10. A program for causing a computer to function as: a request information acquisition means for acquiring request information indicating the design features desired by a user; and an image generation means for generating a second material image showing a design that meets the user's request indicated by the request information, based on the correspondence between a first material image showing the design of the material and the design features of the material indicated by the first material image.

Images