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

Abstract

To provide a material image generation system, a material image generation method, and a program capable of reducing design production costs.SOLUTION: A material image generation system includes: a desired information acquisition part for acquiring desired information indicating design features desired by a user; and an image generation part. On the basis of correlation of correspondence between a first material image indicating a design of a material and characteristics of the design of the material indicated by the first material image, the image generation part generates a second material image indicating a design in accordance with the user's desire indicated by the desired information.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a material image generation system, a material image generation method, and a program. [Background technology]

[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, resulting in 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 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. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 3966919 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 above cannot reflect customer requests in the generated design, so businesses cannot obtain designs that satisfy customer 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 it 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. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, one embodiment of the material image generation system of the present invention is a material image generation system that includes 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 embodiment of the present invention is a computer-executed material image generation method that includes a request information acquisition process for acquiring request information indicating 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 requests indicated by the request information based on a 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 embodiment 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. [Effects of the Invention]

[0010] According to the present invention, the cost of producing a design can be reduced. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing an overview of a building material design output service according to an embodiment of the present invention. [Figure 2] 1 is a block diagram illustrating an example of the configuration of a material image generation system according to an embodiment of the present invention. [Figure 3] FIG. 2 is a block diagram showing an example of the functional configuration of the design learning device according to the present embodiment. [Figure 4] FIG. 2 is a block diagram showing an example of the functional configuration of the material image generating device according to the present embodiment. [Figure 5] 10 is a flowchart illustrating an example of the flow of a design learning process according to the present embodiment. [Figure 6] 10 is a flowchart showing an example of the flow of a material image generation process according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In this embodiment, a material image generation system that generates an image showing a material design (hereinafter also referred to as a "material image") will be described. The material is, for example, wood grain, stone, fabric, etc. In the following, this embodiment will be described by taking as an example an example where 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 businesses that handle building materials provide designs for building materials to users (customers). Building materials include, for example, flooring, decorative paper, wallpaper, and interior decoration materials. Decorative sheets are used for the surface decoration of building materials. The material image generation system generates a material image showing a design applicable to the design of a building material in response to a user's request, and outputs the generated material image to the user. The user can apply the generated material design to the design of 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.

[0013] <1. Overview of the 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.

[0014] In the building material design output service SA shown in FIG. 1, a material image generation model MD that has learned about generating building material designs based on a learning 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 labeled as label information include, for example, color, surface treatment, and the presence or absence of knots.

[0015] 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.

[0016] The material image generation model MD generates and outputs a material image MG2 (second material image) showing a design that meets the user's needs, based on the request information input by the user (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.

[0017] 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).

[0018] 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, it is a design that is easy to reach agreement on. This reduces the frequency with which the material image MG2 is regenerated due to a lack of agreement on the design. On the other hand, if no agreement can be reached on the design, the user can easily regenerate the material image MG2 with a design that more closely matches the user's wishes by adjusting the requests that are input.

[0019] <2. Material Image Generation System Configuration> 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 the outline of which has been explained with reference to FIG.

[0020] As shown in FIG. 2, the material image generating system 1 includes an administrator terminal 10, a user terminal 20, a design learning device 30, and a material image generating device 40. The network NW may be configured to transmit and receive information using, for example, a LAN (Local Area Network), a WAN (Wide Area Network), a telephone network (such as a mobile phone network or a fixed telephone network), a regional IP (Internet Protocol) network, or the Internet.

[0021] (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 a network NW.

[0022] 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.

[0023] The learning 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 labels the training material images MG1 based on the perspective of applying the design of the material to the design of building materials. The label information is prepared by, for example, a designer with experience in designing building materials, who extracts design features from each material image MG1. The material image generation model MD is generated by the design learning device 30 based on the learning material image MG1 and label information transmitted from the administrator terminal 10.

[0024] 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 received by the administrator terminal 10 from the design learning device 30.

[0025] Various UIs (User Interfaces) are displayed on the administrator terminal 10 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. 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.

[0026] (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 a network NW.

[0027] In communication with the material image generation device 40, the user terminal 20 transmits desired information and receives the material image MG2. The user operates the user terminal 20 to input desired information indicating the desired design features and transmits the information to the material image generation device 40. The material image MG2 is generated by the material image generating device 40 based on request information transmitted from the user terminal 20.

[0028] 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. The functions of the user application may be provided by installing the user application on the user terminal 20 (i.e., a native application), or may be provided by a Web system (i.e., a Web application). In the case of a Web application, the user application is managed by a server, and its functions are provided via a Web browser.

[0029] (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 communicably connected to the administrator terminal 10 via a network NW.

[0030] 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.

[0031] (4) Material Image Generator 40 The material image generating device 40 is a device that generates a material image MG2 of 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 a network NW.

[0032] The material image generation device 40 receives the material image generation model MD through communication with the administrator terminal 10. The material image generation device 40 uses the material image generation model MD received from the administrator terminal 10 to generate a material image MG2.

[0033] In communication with user terminal 20, material image generation device 40 receives request information and transmits material image MG2. Material image generation device 40 generates material image MG2 by inputting the request information received from user terminal 20 into material image generation model MD.

[0034] 3. Functional configuration of the 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.

[0035] (1) Communications 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.

[0036] (2) Storage section 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.

[0037] (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 a plurality of material images MG1 with different combinations of material and design features.

[0038] (2-2) Learning 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, the training dataset DS created by the data processing unit 332 described below. The training dataset storage unit 322 stores, as the training dataset DS, a plurality of data labeled with label information for each material image MG1 stored in the material image storage unit 321.

[0039] (2-3) Material Image Generation Model Storage Unit 323 The material image generation model storage unit 323 has a 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.

[0040] The reference model MD1 is a model that has learned about correspondences between design features of materials shown in the training material images MG1 using a training dataset DS in which the training material images MG1 are 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.

[0041] 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 material image MG1 is labeled as label information. That is, 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).

[0042] (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 CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) that the design learning device 30 has as hardware 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.

[0043] (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 that the communication unit 310 receives from the administrator terminal 10.

[0044] (3-2) Data processing unit 332 The data processing unit 332 has a function of performing various types of data processing. For example, the data processing unit 332 creates a training dataset DS based on the training material images MG1 and label information acquired by the data acquisition unit 331.

[0045] (3-3) Learning Section 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 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.

[0046] When generating a 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.

[0047] When generating the additional model MD2, the learning unit 333 learns the correspondence between the training material image MG1 and the design features by using only the training dataset DS stored in the training dataset memory unit 322, for example, that has specific features labeled as label information. As an example, the learning unit 333 extracts, from all learning datasets DS, a learning dataset DS in which "knot presence" is labeled as a design feature for the learning material image MG1. When learning is performed using this extracted learning dataset DS, the learning unit 333 can generate an additional model MD2 that is more likely to output a material image MG2 with a design that has knots. In this way, the learning unit 333 generates and prepares an additional model MD2 for each of multiple specific features.

[0048] 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 about the correspondence relationship. This allows the material image generation model MD to generate and output a high-resolution material image MG2.

[0049] (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.

[0050] 4. Functional Configuration of Material Image Generator 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 generation 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 generation 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.

[0051] (1) Communications Department 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.

[0052] (2) Storage section 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.

[0053] (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 generated by the design learning device 30 and received by the communication unit 410 from the administrator terminal 10.

[0054] (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).

[0055] (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.

[0056] (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.

[0057] (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, the image generating unit 432 can generate a material image MG2 with a high level of design that matches the user's image by using the request information.

[0058] The image generation unit 432 generates the material image MG2 using the reference model MD1, which has been trained on 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.

[0059] (3-3) Model output control unit 433 The model output control unit 433 has a 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 use the additional model MD2 to generate and output a material image MG2 with a design that is closer to the specific feature. This allows the model output control unit 433 to output the material image MG2 that matches the user's image with higher accuracy.

[0060] (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 causes the user terminal 20 to display it.

[0061] <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.

[0062] (1) Design learning processing The flow of the design learning process 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 process according to this embodiment.

[0063] 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 transmitted by the administrator from the administrator terminal 10 to the design learning device 30 and received by the communication unit 310.

[0064] 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.

[0065] 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 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.

[0066] 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 generated by the learning unit 333 to the material image generating device 40 via the administrator terminal 10.

[0067] Next, the learning unit 333 extracts a training dataset DS (step S106). Specifically, the learning unit 333 extracts a training dataset DS in which a specific feature designated by, for example, an administrator is labeled as label information from all the training datasets DS.

[0068] 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 images 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 generating device 40 via the administrator terminal 10.

[0069] (2) Material image generation processing The flow of the material image generation process 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 process according to this embodiment.

[0070] 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.

[0071] Next, the model output control unit 433 of the material image generating device 40 checks whether the user's request indicated by the request information acquired by the request information acquisition unit 431 includes a specific feature (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.

[0072] When 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 about 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.

[0073] 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 desired information acquired by the desired information acquisition unit 431 into the reference model MD1. In addition, 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.

[0074] Next, output processing unit 434 of material image generating device 40 transmits material image MG2 generated by image generating unit 432 from communication unit 410 to user terminal 20, and causes it to be displayed on user terminal 20 (step S206).

[0075] The processing flow according to this embodiment has been described above. As described above, the material image generation system 1 of this embodiment comprises a request information acquisition unit 431 that acquires request information indicating the design features desired by the 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.

[0076] With this 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 have to go through the cycle of creation, proposal, and evaluation in the design creation process, thereby shortening the time required for the design creation process. Therefore, the material image generation system 1 according to this embodiment makes it possible to reduce the cost of creating designs.

[0077] Furthermore, the material image generation system 1 according to this embodiment generates a material image MG2 using a reference model MD1 that is trained using a high-resolution training material image MG1 without reducing it, thereby outputting a high-resolution material image MG2. This allows the user to obtain a high-resolution material image MG2 that can withstand plate-making.

[0078] 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. This 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.

[0079] In addition, the material image generation system 1 of this embodiment reduces the time required for the design production process, thereby achieving overwhelming efficiency in the design production process, and also makes it possible to expand the user's creativity and support the realization of ideal surface designs or spaces. Furthermore, users can take advantage of the unique features (high resolution, high design quality, etc.) of the output material images and use them in digital content.

[0080] <6. Variations> 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 embodiments alone or in combination with each other. Furthermore, each modification may be applied in place of the configuration described in the embodiments, or may be applied in addition to the configuration described in the embodiments.

[0081] 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 present invention is not limited to such an example. For example, the material image generation system may be applied to a service provided in a virtual space (metaverse space).

[0082] 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 (design learning device 30 and material image generating device 40), but the present invention is not limited to such an example. For example, the design learning function and the material image generating function may be implemented by a single device having both functions.

[0083] 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 such an 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.

[0084] 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 such an example. For example, the design learning device 30 and the material image generation device 40 may be able to communicate with each other via a 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.

[0085] The above describes the modified examples of the embodiment of the present invention. In addition, the material image generation system 1, the administrator terminal 10, the user terminal 20, the design learning device 30, and the material image generation device 40 in the above-described embodiment may be partly or entirely implemented by a computer. In this case, a program for implementing this function may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be read into a computer system and executed. Note that the term "computer system" here includes hardware such as an OS and peripheral devices. Additionally, "computer-readable recording media" refers to portable media such as flexible disks, optical magnetic disks, ROMs, CD-ROMs, etc., and storage devices such as hard disks built into computer systems. Furthermore, "computer-readable recording media" may also include devices that dynamically store programs for a short period of time, such as communication lines when transmitting programs via networks such as the Internet or communication lines such as telephone lines, and devices that store programs for a certain period of time, such as volatile memory within computer systems that serve as servers or clients in such cases. Furthermore, the above program may be one that realizes part of the above-mentioned functions, or may be one that can realize the above-mentioned functions in combination with a program already recorded in a computer system, or may be one that is realized using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0086] 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 that described above, and various design changes can be made within the scope of the gist of the present invention. [Explanation of symbols]

[0087] 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 request information acquisition unit that acquires request information indicating design features desired by a user; an image generating unit that generates a second material image showing a design that meets the user's request indicated by the request information based on a correspondence relationship between a first material image showing a design of the material and a feature of the design of the material indicated by the first material image; A material image generation system comprising:

2. 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; The material image generation system according to claim 1 .

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

4. a model output control unit that controls an output of the first trained model using a second trained model that has learned the correspondence relationship for a specific feature among the design features of the material indicated by the first material image; The material image generation system of claim 2 further comprising:

5. When the request information includes the specific feature, the model output control unit uses the second trained model to control the first trained model to generate and output the second material image having a design that is closer to the specific feature. The material image generation system according to claim 4 .

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

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

8. the label information is information to be labeled with the first material image based on a viewpoint when the design of the material is applied to a design of a building material; 7. The material image generation system according to claim 3 or 6.

9. a request information acquisition step of acquiring request information indicating design features desired by a user; 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 a correspondence between a first material image showing a design of the material and a feature of the design of the material indicated by the first material image; 1. A computer-implemented material image generation method comprising:

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

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