Image generation device and image generation method

Abstract

[Problem] To provide assistance for reducing labor of product development. [Solution] An image generation device (1) comprises: a work data input unit (2); an article selection unit (3); and a merchandise image generation unit (4). An image generation method (10) comprises: a work data input step (20); an article selection step (30); and a merchandise image generation step (40). A virtual store (100) is provided with the image generation device (1). A virtual store management method (110) uses the image generation method (10). Selected drawing: FIG. 16

Description

Image generating device and image generating method

[0001] The present invention relates to an image generation device and an image generation method, and more particularly to an image generation device and method that generate product images that match user preferences using an artificial intelligence technique.

[0002] Providing products and services that maximize user satisfaction is one of the goals of all commercial transactions. In particular, when providing products that are closely related to daily life, such as clothing, tableware, food, furniture, household goods, and housing, being preferred by product users, including sellers and consumers, will increase the market competitiveness of the product.

[0003] Over the long history of human commerce, the so-called "order taker" developed as a profession responsible for collecting and analyzing user preferences and orders in order to plan and design products, and so-called "salespeople" still play this role today. In the case of the highly preference-driven products mentioned above, the key goal of product development is to strike a balance between achieving acceptance by as many users as possible (global) and satisfying the preferences of specific users (customization). In either approach, it is necessary to predict the product specifications preferred by users and verify whether the predicted product is acceptable to those users. Today, with the expansion of markets for goods and services and advances in internet technology, various methods have been developed to mechanically or automatically propose products that are highly preferred by users, based on data accumulated through market research and communication with users, replacing the traditional face-to-face approach.

[0004] For example, in the product sample presentation system described in Patent Document 1, the system generates sample images of the product combined with other tableware and ingredients based on product data entered by the user on a terminal, effectively promoting the product to the user. This presentation system solves common problems when purchasing tableware, such as "I bought it because I thought it looked nice, but it didn't match the tableware I have at home," or "I bought it without thinking about how the food would look on it, and then realized later that I don't have a chance to use it in my daily meals." As a result, each user can select products by imagining how they will actually be used, resulting in a more satisfying shopping experience.

[0005] In the product information provision system described in Patent Document 2, users can interact with the system via a product search terminal, and the system analyzes the voice and images provided by the user to recommend products that meet the user's needs. This product information provision system uses so-called two-way information communication to provide each user with a service environment similar to face-to-face service provided by a store salesperson. As a result, users can experience so-called "attentive service" even in large stores with few store sales signs, resulting in highly satisfying shopping experiences.

[0006] The method and device described in Patent Document 3 apply virtual reality to place images of items such as furniture and interior fittings on an image of a room, efficiently generating a highly accurate image showing how a vacant room would look with model furniture actually placed in it. This method and device allows a user to imagine what real life would be like after moving into a vacant room without actually placing model furniture in the room, and allows the user to efficiently search for and select a vacant room that suits their preferences.

[0007] The applicant also previously proposed and patented an image information processing device and method in Japanese Patent Application No. 2018-018849 (Patent Document 4). This device and method generate an image of a two-dimensional drawing, such as a design or illustration, printed on a three-dimensional object such as a T-shirt. As a result, users can predict the finished product before prototyping it, enabling product development at low cost.

[0008] Furthermore, the applicant proposed a method and apparatus for operating a virtual shop in International Patent Application PCT / JP2022 / 009001 (Patent Document 5). The virtual shop envisioned here displays image data of a product (C) in which a specific image (A) is applied to a specific item (B), and users can review the image data and order the product (C). The method and apparatus proposed here can show users a realistic appearance of the product (C) by reproducing the wrinkles and shading of a fabric product such as a T-shirt using image data of the product (C).

[0009] These examples are methods for generating virtual images of products that match the user's preferences when the products to be recommended to the user have been determined in advance. Users who use these methods must select in advance the product for which they want to obtain information from three-dimensional objects such as tableware, mass-produced products such as home appliances sold in large stores, vacant rooms, and T-shirts.

[0010] Meanwhile, the triggers for new product creation have been changing recently. In the past, new products were introduced by improving specific products with the goal of "selling good things." However, in recent years, the appeal of a product is sometimes determined not in its basic functionality or quality, but in the artwork and design attached to the product. A typical example is so-called character goods. The appeal of character goods lies in the specific, unique illustrations that are adorned on the goods. The product can be thought of as a vehicle for delivering the illustration to consumers and allowing them to experience the image of the illustration. When a product called character goods is released, an attractive illustration or design (hereinafter referred to as "artwork") is first completed, and then an item is selected to feature the illustration, and the product with the illustration (hereinafter referred to as "goods") is produced.

[0011] In recent years, online social networking systems have provided freelance artists, designers, and even individuals without special contracts with others with the opportunity to exhibit their work. Furthermore, online trading systems have also created an environment in which a wide variety of products can be bought and sold in small quantities. As a result, freelance artists and other professionals who do not have the means to produce products, art enthusiasts, students, and other individual creators (hereafter referred to as "creators" in this specification) can incorporate their own work into "some kind of product" and attempt to commercialize their own work, i.e., to manufacture goods.

[0012] However, as mentioned above, conventional methods of providing product information using virtual reality are limited to specific products, and they cannot quickly respond to creators' requests to "make some attractive goods" or "attach them to products that are likely to sell." Today, with the ever-increasing speed of information transmission, creators want to quickly turn their highly acclaimed works into merchandise, but they often lack knowledge of the products that should or can be equipped with their works. This may result in creators losing opportunities to turn their attractive works into merchandise, and general consumers losing opportunities to discover attractive new products.

[0013] On the other hand, consumer tastes are becoming more diverse. Consumer preference criteria vary, including "everyone has it," "recommended by someone I admire," "ahead of the trends," "as a symbol of friendship," and "my own personal favorite." Therefore, methods have been proposed to provide products customized to consumer tastes using computers and the Internet.

[0014] In the clothing production system described in Patent Document 6, for each order, the printing device receives a color / pattern printing signal from the management computer according to the color and pattern selected by the customer and prints it on each piece of fabric (raw material).

[0015] In the embroidery system described in Patent Document 7, a user selects a garment via the Internet, specifies embroidery locations on the garment, and selects an embroidery pattern. A three-dimensional embroidery simulation is displayed. An embroidery pattern control signal is provided to a remote embroidery machine via the Internet, allowing the user's customized embroidery pattern to be automatically sewn onto the actual garment.

[0016] In a system for custom-making clothing at a retail facility described in Patent Document 8, a customer selects a design from a catalog, a computing device transmits printing instructions associated with the customer's selected design to a printer, and the printer applies colorant to the clothing based on the printing instructions to reproduce the selected design on the clothing.

[0017] Patent Document 9 describes an umbrella ordering processing method that enables a customer to specify an image for the umbrella fabric and order a one-piece original umbrella online.

[0018] However, even the custom manufacturing mentioned above targets consumers who have already decided on a product type, such as clothing or an umbrella, and cannot quickly respond to vague requests such as "I want to have some attractive goods" or "I want to make a souvenir that will be appreciated." In today's world where the speed of information transmission is ever-increasing, consumers want to obtain products that satisfy them as quickly as possible, but it is a burden for them to select goods that feature the desired patterns, symbols, and designs and that will be objectively and favorably evaluated by those around them. As a result, even if individual consumers strongly desire "goods that are unique to them," there is a lack of means to accurately provide such goods.

[0019] Japanese Patent Application Laid-Open No. 2019-191860 Japanese Patent Application Laid-Open No. 2020-52833 Patent Registration No. 7001138 Patent Registration No. 6877719 International Patent Application PCT / JP2022 / 009001 Japanese Patent Application Laid-Open No. 2006-252402 Japanese Patent Application Laid-Open No. 2001-314677 Japanese Patent Application Laid-Open No. 2013-511093 Japanese Patent Application Laid-Open No. 2020-77252

[0020] Regardless of whether it is for commercial purposes or not, individuals and corporations who wish to produce goods, such as creators, licensors, consumers, and sellers (hereinafter referred to as "users"), can introduce attractive new products to the market if they can appropriately select goods that best embody the appeal and preferences of their own works, even if they do not have specialized knowledge or experience in product development. In this case, if the costs of prototyping and evaluating new products can be reduced, the chances of successful commercialization of the works can be further increased.

[0021] The inventors sought an advanced method to respond to users' straightforward requests for "I just want to make nice-looking goods, whatever they like." As a result, they discovered a method for proposing goods that meet users' preferences while minimizing their effort. That is, the present invention is as follows. (Invention 1) An image generation device (1) having a work data input unit (2), an item selection unit (3), and a goods image generation unit (4), wherein the work data input unit (2) acquires image data (work data) (22) of a work (21) designated by a user and, optionally, data related to the user (user data) (23), the item selection unit (3) automatically selects an item (31) to which the work (21) is attached based on the work data (22) and the optional user data (23) and acquires image data (item data) (32) of the item (31), and the goods image generation unit (4) uses the work data (22) and the item data (32) to generate an image (goods image) (42) of goods (41) in which the work (21) is attached to the item (31). (Invention 2) The image generation device (1) of Invention 1, wherein the work data (22) in the work data input unit (2) is image data of a two-dimensional work (21). (Invention 3) The image generation device (1) of Invention 1, wherein an artificial intelligence (33) selects an item (31) in an item selection unit (3). (Invention 4) The image generation device (1) of Invention 1, wherein a goods image generation unit (4) generates one or more goods images (42) for each of one or more goods (41). (Invention 5) An image generation method (10) comprising a work data input step (20), an item selection step (30), and a goods image generation step (40), wherein in the work data input step (20), image data (work data) (22) of a work (21) designated by a user and, optionally, data related to the user (user data) (23) are acquired; in the item selection step (30), an item (31) to which the work (21) is to be attached is automatically selected based on the work data (22) and the optional user data (23), and image data (item data) (32) of the item (31) is acquired; and in the goods image generation step (40), an image (goods image) (42) of goods (41) in which the work (21) is attached to the item (31) is generated using the work data (22) and the item data (32).(Invention 6) The image generation method (1) of Invention 5, wherein in the work data input step (20), the work data (202) is image data of a two-dimensional work (201). (Invention 7) The image generation method (10) of Invention 5, wherein in the item selection step (30), an artificial intelligence (303) selects an item (301). (Invention 8) The image generation method (10) of Invention 5, wherein in the item image generation step (40), one or more item images (402) are generated for each of one or more items (401).

[0022] According to the present invention, it is possible to efficiently propose goods that are highly likely to satisfy the user's needs.

[0023] Reference diagram for understanding Reference Method 1 and Reference Apparatus 1 related to the present invention. Reference diagram for understanding Reference Method 1 and Reference Apparatus 1 related to the present invention. Reference diagram for understanding Reference Method 1 and Reference Apparatus 1 related to the present invention. Reference diagram for understanding Reference Method 1 and Reference Apparatus 1 related to the present invention. Reference diagram for understanding Reference Method 1 and Reference Apparatus 1 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. Reference diagram for understanding Reference Method 2 and Reference Apparatus 2 related to the present invention. A reference diagram for understanding the image generation method (10) of the present invention. A reference diagram for understanding the virtual shopping mall (100) of the present invention. An example of a reference image (7) that can be added to a goods image (42) in the present invention.

[0024] [Image generation device (1), image generation method (10)] The image generation device (1) of the present invention has a work data input unit (2), an item selection unit (3), and a goods image generation unit (4). Figure 16 shows the concept of the image generation device (1). The image generation method (10) of the present invention has a work data input step (20), an item selection step (30), and a goods image generation step (40). Figure 17 shows the concept of the image generation method (10).

[0025] The work data input step (20) is executed by the work data input unit (2). The item selection step (30) is executed by the item selection unit (3). The goods image generation step (40) is executed by the goods image generation unit (4).

[0026] [Work Data Input Unit (2), Work Data Input Step (20)] In the work data input unit (2) or work data input step (20), image data (work data) (22) of the work (21) designated by the user is acquired. The work (21) can be of any type as long as the work data (22) is acquired as image data. Generally, it is a two-dimensional work such as an illustration or photograph that users often attempt to turn into merchandise. The format of the work data (22) is not limited as long as it is a known image data format.

[0027] In some cases, the work data input unit (2) and the work data input step (20) may also acquire user-related data (user data) (23). The user data (23) is personal or commercial information about users (hereinafter referred to as "users") of the image generation device (1), image generation method (10), or virtual shop (100) or virtual shop operation system (110) equipped with the image generation device (1), image generation method (10), or virtual shop (100) equipped with the image generation device (100), image generation method (100), or virtual shop operation system (110), including the image generation device (100), image generation method (100), or virtual shop operation system (110), depending on the user's circumstances. In other words, the user data (23) is information that can be referenced to predict the user's evaluation of the work (21), the user's preferences for items (31), and the user's taste in goods (41). A typical user data (23) is a product group that serves as the population of items (31).

[0028] For example, the product data input unit (2) and the product data input step (20) acquire information on one or more of the user's desired items, such as "clothing," "stationery," "food," "household goods," and "tableware." Other typical user data (23) includes information on the user's intended buyer demographic, such as age, gender, and region. The user data (23) may also include the desired price and delivery date for the goods. The user data (23) may also include one or more product images or keywords selected from the following categories: "cute," "adult-like," "artistic," "luxurious," "relaxed," "smart," "healthy," "regional," and "environmentally conscious." A system may be provided in which the user can choose whether or not to provide the user data (23). In this case, the user can specify whether or not to provide the user data (23) and the items of the user data (23) themselves.

[0029] The artwork data (22) and user data (23) are generally transmitted from a user's mobile device or PC. For example, a user may transmit photographic data of the artwork (21) to the image generating device (1) as artwork data (22). The user may also select and specify the artwork data (22) from image data stored in a cloud on the Internet.

[0030] The work data (22) acquired in the work data input section (2) and work data input step (20), or the data set of the work data (22) and user data (23), is used in the next item selection section (3) and item selection step (30).

[0031] [Item Selection Unit (3), Item Selection Step (30)] In the item selection unit (3) or item selection step (30), an item (31) to which the work (21) is to be attached is automatically selected based on the work data (22) and arbitrary user data (23), and image data (item data) (32) of the item (31) is obtained.

[0032] The automatic selection of the item (31) is performed by so-called artificial intelligence (33). The artificial intelligence (33) uses a certain type of item prediction model (34) to extract image data (item data) (32) of the item (32) that has the highest user satisfaction for the work data (22). The prediction model (34) is constructed using training data related to the work (21), the item (31), the goods (41), users, the goods market, etc. The artificial intelligence (33) can improve the accuracy of the prediction model (34) through machine learning using history information of the image generation device (1) and image generation method (10) of the present invention.

[0033] The features (341) input to the prediction model (34) in the item selection unit (3) and item selection step (30) are extracted by analyzing the artwork data (22) or a dataset of the artwork data (22) and user data (23). Features based on the artwork data (22) are, for example, numerical values ​​assigned to objects (e.g., abstract or concrete, living or non-living), designs (e.g., the ratio of curves to the total length of the line drawing, shapes approximating the main parts of the artwork, such as circles or squares), and colors (e.g., average or main brightness, saturation). By calculating these features, it is also possible to generate higher-level features, such as the intensity of elements constituting the image of the artwork (21) (e.g., cute, mature, artistic, luxurious, relaxed, smart, healthy, local, environmentally conscious). The features based on the user data (23) are, for example, numerical values ​​assigned to consumer attributes (e.g., age, gender, residential area), desired products (e.g., clothing, stationery, food, household goods, tableware), price (e.g., less than 500 yen, 500 to 3000 yen, 3000 to 5000 yen, 5000 yen or more), and images to be emphasized (e.g., cute, mature, artistic, luxurious, relaxed, smart, healthy, local, environmentally conscious). By calculating these features, higher-level features can also be assigned.

[0034] The artificial intelligence (33), for example, introduces the above-mentioned feature (341) into a prediction model (34) and performs calculations to generate image data (item data) (32) of a specific item (31) as item information predicted to be valid. For example, text data strongly related to the feature (341) is generated, and the generated text data is used to search a database to extract data (32) of the item (31) that meets the conditions. Furthermore, for example, by comparing the feature (341) with the item feature registered in the database, the item (31) that has the highest affinity with the work (21) is selected, and its image data (32) is extracted. The database referenced in the search is not particularly limited, and may be a dedicated database attached to the image generation device (1) of the present invention or a database accessible on the Internet (so-called big data).

[0035] In the image generating device (1) of the present invention, the user does not need to directly specify an item to the item selection unit (3). The user only needs to have a vague desire to "want some suitable goods" until the item selection step (30), and does not need to actively consider specific product types such as "clothing" or "stationery."

[0036] A plurality of items (31) can be selected in the item selection section (3) or item selection step (30). The obtained item data (32) is then combined with the artwork data (22) in the next goods image generation section (4) and goods image generation step (40).

[0037] [Goods image generation unit (4), goods image generation step (40)] In the goods image generation unit (4) or the goods image generation step (40), an image (goods image) (42) of goods (41) in which the work (21) is attached to the item (32) is generated using the work data (22) and the item data (32). The goods image (42) is a so-called composite image of the work data (22) and the item data (32). In the goods image generation unit (4) or the goods image generation step (40), the goods image (42) is generally displayed on a display device such as a smartphone or PC.

[0038] The merchandise image generation unit (4) and merchandise image generation step (40) can generate one or more merchandise images (42) for each of one or more merchandise items (41). The merchandise image generation step (40) can generate one or more merchandise images (402) for each of one or more merchandise items (401). In short, in this invention, an artificial intelligence (AI) technique is used to recommend one or more items (31) for one piece of artwork data (22), and one or more merchandise images (42) corresponding to each item (31) are generated. Here, merchandise images (42) are proposed to the user with an appropriate variety, providing the user with appropriate options and encouraging the user to make a highly satisfying selection. For example, if two items, a mug and a T-shirt, are selected as items (31) suitable for one piece of illustration data (22), several patterns of merchandise images (42) are provided that differ from each other in terms of the arrangement of the illustration, the trimming of the illustration background, the brightness and saturation of the arranged design, etc.

[0039] As long as the actual goods (41) can be visually recognized through the goods image (42), known image synthesis techniques can be used without limitation to generate the goods image (42). For example, image trimming techniques, color tone and contrast adjustment techniques, etc. can be used.

[0040] Typically, the product image (42) is displayed on the user's terminal device, and the user can observe the product image (42) while selecting the actual product (41) on the terminal device. The user can decide whether or not to adopt the proposed product (41) without having to make a prototype of the product (41).

[0041] The goods image generation unit (4) and goods image generation step (40) can also generate a reference image (7) showing a person using the goods (41) as an additional operation. The reference image (7) can be, for example, a customer in a cafe holding a mug (41), or a person holding a smartphone (41) with a smartphone case attached. The reference image (7) can be useful as a basis for a user's selection of goods (41). Similar to the generation of the item data (32), the reference image (7) can be synthesized by referencing an appropriate database in a so-called artificial intelligence (AI) predictive manner, predicting situations where the user's preference probability is high. Figure 19 shows an example of the reference image (7). By using the reference image (7), the user can specifically understand how a person handles the mug (41).

[0042] [Reference Example 1] As a means for generating the goods image (42) in the goods image generation unit (4) and the goods image generation step (40), the image information processing device (hereinafter referred to as "Reference Device 1") and the image information processing method (hereinafter referred to as "Reference Method 1") proposed by the applicant in Patent Document 4 can be used. Reference Device 1 has the following processing units.

[0043] First layer creation processing unit: Creates a first layer of only the background from the original image information of a specific item by making the main body of the item transparent. Second layer creation processing unit: Selects a color gamut range from the main body color of the item in the original image information of the item to a predetermined range, and then creates a second layer by inverting and copying the selected color gamut range. Color correction layer creation processing unit: Creates a color correction layer by color correcting the second layer to a predetermined brightness and contrast. Combined layer creation processing unit: Creates a combined layer by combining the second layer and the color correction layer. Transparency correction layer creation processing unit: Creates a transparency correction layer by adjusting the transparency of the second layer in the combined layer to a predetermined transparency ratio. Post-processing template creation processing unit: Creates a post-processing template by overlaying the first layer on top of the second layer in the transparency correction layer. Combined image information creation processing unit: Creates combined image information by adding and combining the post-processing template with the desired image information.

[0044] The second layer creation processing unit can select a portion having a color within a specific error range for each of the R / G / B channels from the main color sampled from the main portion. The color correction layer creation processing unit can reduce color values ​​to widen shadow areas or expand the entire range of color values. The transparency correction layer creation processing unit can apply a predetermined alpha value to all second pixels in the combined layer.

[0045] Reference method 1 includes the following steps: Creating a first layer of only the background from original image information of a specific item by making the main body of the item transparent; Selecting a color gamut range from the main body color of the item in the original image information of the item to a predetermined range, and then creating a second layer by inverting and copying the selected color gamut range; Color-correcting the second layer to a predetermined brightness and contrast to create a color-corrected layer; Merging the second layer and the color-corrected layer to create a combined layer; Setting the transparency of the second layer in the combined layer to a predetermined percentage to create a transparency-corrected layer; Overlaying the first layer on top of the second layer in the transparency-corrected layer to create a processed template; Adding and compositing the processed template to the desired image information to create combined image information.

[0046] The color gamut range selection may select a portion having a color within a specific error range for each of the R / G / B channels from the main color sampled from the main portion. The color correction may be a process of reducing color values ​​and widening shadow areas, or a process of expanding the entire range of color values. The step of creating the transparency correction layer may be a process of applying a predetermined percentage alpha value to all second pixels in the combined layer.

[0047] The reference device 1 is a device that displays desired image information superimposed on image information about a specific item, and is configured, for example, by a computer.

[0048] The reference device 1 is configured, for example, by a computer, and although not shown, includes a CPU that performs control to realize the various functions of the reference device 1 and the reference method 1 in accordance with an operating system and a specific computer program that has been installed or downloaded, a ROM that stores the operating system and various computer programs, etc., and serves as a storage unit for storing data necessary for the CPU to execute processes for each control, a RAM and a hard disk that store data necessary for the CPU to execute processes and are also used as a work area where information can be rewritten by the CPU as appropriate, a user interface, a wireless communication interface, etc., all of which are connected by the necessary bus lines.

[0049] 1 includes an information input unit 2', an information output unit 3', an information transmitting / receiving unit 4', a first layer production processing unit 5', a second layer production processing unit 6', a color correction layer production processing unit 7', a combined layer production processing unit 8', a transparency correction layer production processing unit 9', a post-processing template production processing unit 10', a post-combination image information production processing unit 11', and a memory unit 20'. Note that hereinafter in this specification, the first layer production processing unit 5' and the second layer production processing unit 6' may be referred to as the first layer production processing unit 5' and the second layer production processing unit 6', respectively.

[0050] The information input unit 2' constituting the user interface is composed of, for example, a keyboard, a mouse, a scanning device, etc. Similarly, the information output unit 3' constituting the user interface is composed of, for example, a computer display screen.

[0051] The information transmission / reception unit 4' transmits and receives necessary information, such as image information, to and from external server computers, computers used by users of reference method 1, and terminals such as smartphones via network communication networks such as the Internet communication network and wireless communication networks defined by wireless communication standards.

[0052] By having the business operator providing Reference Method 1 connect Reference Device 1 to a network such as the Internet and use it as a server device, users who wish to use Reference Method 1 can access the website where Reference Method 1 is provided from their own computer, smartphone, etc. via a network such as the Internet and use Reference Method 1.

[0053] In other words, a user of Reference Method 1 can use a web browser implemented on the user's computer, smartphone, etc. to access a web server set up by the provider of Reference Method 1 by connecting Reference Device 1 to a network such as the Internet and using it as a server device, and can then use the web browser to perform a process of overlaying preferred image information stored on the user's computer, smartphone, etc. onto image information of various specific items stored in the memory unit 20' of Reference Device 1.

[0054] The first layer creation processing unit 5' of the reference device 1 creates a first layer of only the background from the original image information of a specific item by making the main body of the item transparent.

[0055] Examples of specific items include accessories such as can badges and pin badges, mobile terminal cases for storing or attaching smartphones and mobile phones, various cases such as pass cases, business card cases, eyeglass cases, seal cases, and pen cases, tableware and tableware such as mugs and tumblers, clothing such as T-shirts, hoodies, and handkerchiefs, bags and pouches such as tote bags and pouches, stationery such as masking tape and mouse pads, and various other items on which image information such as photographs and pictures is printed or displayed. These items can be considered as items (31) selected in the item selection unit (3) and the item selection step (30).

[0056] The second layer creation processing unit 6' selects a color gamut range from the body color of the item in the original image information of the item to a predetermined range, and then creates a second layer by inverting and copying the selected color gamut range.

[0057] Here, the second layer creation processing unit 6' selects a portion having a color within a specific error range for each of the R (red), G (green), and B (blue) channels from the body color sampled from the body portion of the article. For example, the second layer creation processing unit 6' selects a portion having a color within an error range of 70 for each of the R (red), G (green), and B (blue) channels from the body color sampled from the body portion of the article.

[0058] The original image information of the specific item described above, which is handled by the first layer production processing unit 5' and the second layer production processing unit 6', is image information obtained by photographing a white item by shining light from a light source on it, as in a normal photograph, such as the image information of a leather pass case, the reference photograph of which is shown in Figure 3.

[0059] This image information may be created in advance for each of the above-mentioned articles and stored in the storage unit 20 ′ of the reference device 1 .

[0060] The color-tone correction layer production processing unit 7' produces a color-tone correction layer by correcting the second layer to a predetermined brightness and a predetermined contrast.

[0061] Here, the color correction layer creation processing unit 7' performs a process of reducing color tone values ​​and widening shadow areas and / or a process of expanding the entire range of color tone values. For example, when Photoshop (registered trademark) is used and both the process of reducing color tone values ​​and widening shadow areas and the process of expanding the entire range of color tone values ​​are performed, color correction is performed to brightness -150 by the process of reducing color tone values ​​and widening shadow areas, and to contrast 100 by the process of expanding the entire range of color tone values.

[0062] The combined layer creation processing unit 8' creates a combined layer by combining the second layer and the color correction layer.

[0063] The transparency correction layer creation processing unit 9' creates a transparency correction layer by setting the transparency of the second layer in the combined layer to a predetermined percentage of transparency.

[0064] Here, the transparency correction layer creation processing unit 9′ applies a predetermined ratio of α values ​​to all the second pixels in the combined layer, for example, applying an α value of about 80% to all the second pixels in the combined layer.

[0065] The post-processing template creation processing unit 10' creates a post-processing template by laying the first layer on top of the second layer in the transparency correction layer.

[0066] The synthesized image information creation processing unit 11' creates synthesized image information by adding and combining the processed template with the desired image information.

[0067] The created composite image information is referred to as an appearance characteristic layer, and the reference photograph is shown in Fig. 6. The composite image information (appearance characteristic layer) shown in the reference photograph in Fig. 6 holds information on both the light source effect and texture, allowing a user of Reference Method 1 to visually confirm and grasp what kind of light source effect and texture will be exhibited when the user superimposes his / her preferred image information (in this embodiment, the image information shown in the reference photograph in Fig. 5) on the specific item described above (in Fig. 6, the leather pass case shown in the reference photograph in Fig. 3).

[0068] Next, an example of the reference method 1 using the above-mentioned reference device 1 will be described with reference to FIG.

[0069] Here, we will explain the case where image information of a leather pass case, the reference photograph of which is shown in Figure 3, is superimposed on image information provided by the user, the reference photograph of which is shown in Figure 5.

[0070] First, the first layer creation processing unit 5' creates a first layer of only the background from the original image information of the leather pass case, making the main body transparent (S101).

[0071] Next, the second layer creation processing unit 6' selects a color gamut range from the body color in the original image information of the leather pass case to a predetermined range. That is, a process is performed to select portions of the body color sampled from the body portion of the item that have a color within a specific error range for each of the R (red), G (green), and B (blue) channels. For example, portions of the body color sampled from the body portion of the item that have a color within an error range of approximately 7% for each of the R (red), G (green), and B (blue) channels are selected. For example, if Photoshop (registered trademark) is used, portions of the color within an error range of 70% are selected. Furthermore, a second layer is created by inverting and copying the selected color gamut range (S102).

[0072] Next, the color correction layer creation processing unit 7' performs color correction on the second layer to a predetermined brightness and a predetermined contrast. That is, a process of reducing color values ​​and widening shadow areas and / or a process of expanding the entire range of color values ​​is performed. For example, when Photoshop (registered trademark) is used and both a process of reducing color values ​​and widening shadow areas and a process of expanding the entire range of color values ​​are performed, color correction is performed to a brightness of -150 by reducing color values ​​and widening shadow areas, and a contrast of 100 by expanding the entire range of color values. In this way, a color correction layer is created (S103).

[0073] Next, the second layer and the color correction layer are combined by the combined layer creation processing unit 8' to create a combined layer (S104).

[0074] Next, the transparency correction layer creation processing unit 9' creates a transparency correction layer by setting the transparency of the second layer in the combined layer to a predetermined percentage of transparency (S105). That is, a process is performed in which a predetermined percentage of α value is applied to all the second pixels in the combined layer. For example, a process is performed in which an α value of about 80% is applied to all the second pixels in the combined layer.

[0075] Next, the post-processing template creation processing unit 10' creates a post-processing template by laying the first layer on top of the second layer in the transparency correction layer (S105). This state is shown in the reference photograph of Figure 4.

[0076] Next, the synthesized image information creation unit 11' adds and combines the processed template shown in the reference photograph of Figure 4 with the image information provided by the user, the reference photograph of which is shown in Figure 5, to create synthesized image information (S106).

[0077] The synthesized image information thus produced is referred to as an appearance characteristic layer, and a reference photograph is shown in FIG.

[0078] As explained above, by creating an appearance characteristic layer from image information obtained by photographing the specific white item described above in a normal manner, it is possible to create an appearance characteristic layer that retains information on both the lighting effect and the texture. This allows the user to visually confirm and understand what lighting effect and texture will be exhibited when an image of the artwork is superimposed on an image of the item.

[0079] As described above, the reference method 1 performed by the reference device 1 is extremely simple in processing and can express complex textures and shadows caused by light sources with just one simple additive synthesis. In other words, highly accurate texture and light source expression can be achieved by simply performing lightweight, simple processing on image information.

[0080] In addition, users of Reference Method 1 will be able to render in real time on their browsers.

[0081] Furthermore, simple additive compositing in this embodiment can be realized by overlaying images converted from luminance to α-value density. By overlaying images converted from luminance to α-value density in the browser, texture and light source expression can be performed for any image at the browser's native speed.

[0082] According to Reference Device 1 and Reference Method 1, even when the server device receives image information from an end user and applies texture expression and light source expression as described above, simple additive synthesis is sufficient as described above, so the server device is not subjected to a heavy load.

[0083] [Reference Example 2] The "data processing method" in the method for operating a virtual shop proposed by the applicant in Patent Document 5 (hereinafter "Reference Method 2") and the "data processing device" in the device for operating the virtual shop (hereinafter "Reference Device 2") can be used as means for generating a goods image (42) in the goods image generation unit (4) and the goods image generation step (40). Reference Method 2 and Reference Device 2 are described below. Image (A) can be treated as an example of the work (21) of the present invention, item (B) as an example of the item (31) of the present invention, and product (C) as an example of the goods (41) of the present invention. While a virtual shop that can use Reference Method 2 and Reference Device 2 is not synonymous with the virtual shop of the present invention, the operation and functions of the "data processing method" in Reference Method 2 and the "data processing device" in Reference Device 2 are effective in generating goods images (42) of the present invention.

[0084] Reference method 2 is a method for operating a virtual store that uses an image data processing method for handling a product (C) in which a specific image (A) is applied to a specific item (B), a terminal of a virtual store user who wishes to purchase or list the product (C), a terminal of a virtual store operator, and a server connected to the Internet.

[0085] The image data processing method includes the following steps: ＊ ~5 ＊ (Step 1) ＊ ) A step of converting image data (A0) corresponding to the image (A) to obtain image data (A1). (Step 2 ＊ ) A step of converting image data (B0) corresponding to the article (B) to obtain image data (B1) corresponding to the surface state of the area of ​​the article (B) on which the image (A) is printed. (Step 3 ＊(Step 4) A step of acquiring image data (A2) from the image data (A1) and the image data (B1). ＊ ) A step of converting the image data (A2) to obtain image data (A3). (Step 5 ＊ ) A step of obtaining image data (C0) corresponding to the product (C) from the image data (A3) and the image data (B0).

[0086] Based on the image data (C0) obtained by the image data processing method, the server displays a sample image of the product (C) on the virtual store user's terminal, and transmits and receives information regarding the purchase and / or listing of the product (C) between the virtual store user's terminal and the virtual store operator's terminal.

[0087] Step 1 ＊ Then, the values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A0) corresponding to the image (A) can be converted to obtain the image data (A1).

[0088] Step 1 ＊ The image data (A1) can be obtained by converting the values ​​corresponding to one or more of the lightness, saturation, and transparency of the image data (A0) corresponding to the image (A) so that one or more of the lightness, saturation, and transparency are reduced.

[0089] Step 2 ＊ Then, image data (B0) corresponding to the item (B) can be converted to obtain image data (B1) corresponding to the unevenness and / or texture of the area of ​​the item (B) to which the image (A) is applied.

[0090] Step 2 ＊ Then, the color image data contained in the image data (B0) corresponding to the item (B) is converted into grayscale image data and / or the image data (B0) is blurred, and image data (B1) corresponding to the unevenness and / or texture of the area on the item (B) where the image (A) is printed can be obtained.

[0091] Step 3 ＊From the image data (A1) and the image data (B1), image data (A2) can be obtained that reflects both the image (A) and the surface condition of the area of ​​the item (B) to which the image (A) is applied.

[0092] Step 3 ＊ From the image data (A1) and the image data (B1), image data (A2) can be obtained that reflects both the image (A) and the area of ​​the item (B) where the image (A) is printed.

[0093] Step 4 above ＊ Then, the image data (A3) can be obtained by converting values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A2).

[0094] Step 4 above ＊ Then, the values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A2) are converted so that one or more of the brightness, saturation, transparency, and sharpness are increased, and the image data (A3) can be obtained.

[0095] Step 5 above ＊ From the image data (A3) and the image data (B0), image data (C0) can be obtained as a sample image of a custom-made product obtained by applying the image (A) to the item (B).

[0096] The image (A) may be one selected by a user who intends to purchase or sell the product (C) in the virtual shop on the user's terminal.

[0097] The information regarding the purchase and / or listing of the product (C) may include one or more of the size, quantity, delivery address, and price of the product (C).

[0098] For example, the article (B) is made of one or more materials selected from the group consisting of resin, metal, ceramic, and natural product. For example, the article (B) is clothing.

[0099] Reference device 2 is a device for operating a virtual store, which includes an image data processing device for handling a product (C) in which a specific image (A) is applied to a specific item (B), a terminal for a virtual store user who wishes to purchase or list the product (C), a terminal for a virtual store operator, and a server connected to the Internet.

[0100] The image data processing device includes the following devices: ＊ ~5 ＊ (Device 1 ＊ ) A device that converts image data (A0) corresponding to the image (A) to obtain image data (A1). ＊ ) A device that converts image data (B0) corresponding to the item (B) to obtain image data (B1) corresponding to the surface condition of the area of ​​the item (B) where the image (A) is printed. (Device 3 ＊ ) A device for acquiring image data (A2) from the image data (A1) and the image data (B1). (Device 4 ＊ ) A device that converts the image data (A2) to obtain image data (A3). (Device 5 ＊ ) A device for acquiring image data (C0) corresponding to the product (C) from the image data (A3) and the image data (B0).

[0101] Based on the image data (C0) obtained by the image data processing method, the server displays a sample image of the product (C) on the virtual store user's terminal, and transmits and receives information regarding the purchase and / or listing of the product (C) between the virtual store user's terminal and the virtual store operator's terminal.

[0102] The above-mentioned device 1 ＊ Then, the values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A0) corresponding to the image (A) can be converted to obtain the image data (A1).

[0103] The device 1 can obtain the image data (A1) by converting values ​​corresponding to one or more of the brightness, saturation, and transparency of the image data (A0) corresponding to the image (A) so that one or more of the brightness, saturation, and transparency are reduced.

[0104] The above device 2 ＊ Then, image data (B0) corresponding to the item (B) can be converted to obtain image data (B1) corresponding to the unevenness and / or texture of the area of ​​the item (B) to which the image (A) is applied.

[0105] The above device 2 ＊ Then, the color image data contained in the image data (B0) corresponding to the item (B) is converted into grayscale image data and / or the image data (B0) is blurred, and image data (B1) corresponding to the unevenness and / or texture of the area on the item (B) where the image (A) is printed can be obtained.

[0106] The above device 3 ＊ From the image data (A1) and the image data (B1), image data (A2) can be obtained that reflects both the image (A) and the surface condition of the area of ​​the item (B) to which the image (A) is applied.

[0107] The above device 3 ＊ From the image data (A1) and the image data (B1), image data (A2) can be obtained that reflects both the image (A) and the area of ​​the item (B) where the image (A) is printed.

[0108] The above device 4 ＊ Then, the image data (A3) can be obtained by converting values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A2).

[0109] The above device 4 ＊ Then, the values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A2) are converted so that one or more of the brightness, saturation, transparency, and sharpness are increased, and the image data (A3) can be obtained.

[0110] The above device 5 ＊From the image data (A3) and the image data (B0), image data (C0) can be obtained as a sample image of a custom-made product obtained by applying the image (A) to the item (B).

[0111] The image (A) may be one selected by a user who intends to purchase or sell the product (C) in the virtual shop on the user's terminal.

[0112] The information regarding the purchase and / or listing of the product (C) may include one or more of the size, quantity, delivery address, and price of the product (C).

[0113] The article (B) is made of one or more materials selected from the group consisting of resin, metal, ceramic, and natural product, and is, for example, an article of clothing.

[0114] Reference method 2 and reference device 2 will be described in detail below.

[0115] [Image (A), Item (B), Product (C)] The image data processing method and device are for handling a product (C) in which a specific image (A) is applied to a specific item (B). The specific image (A) is any visible object that has design or information that can be visually evaluated and identified, including, for example, paintings, photographs, books, printed materials of these, and digital data of these displayed on a mobile device such as a PC or smartphone. The specific item (B) is any medium on whose surface the image (A) can be printed directly or indirectly.

[0116] In the case of direct printing, so-called digital printing is used, specifically, image data based on image (A) is sent as printing data to a printer via the Internet, intranet communication, or electronic media, and the printer uses the received printing data to control the output of colorants such as ink to print the desired image (A) on the surface of the item (B). In the case of indirect printing, image data based on image (A) is sent as printing data to a printer via the Internet, intranet communication, or electronic media, and the printer uses the received printing data to control the output of colorants such as ink to print the desired image (A) on a laminate substrate such as a film or sheet, and the resulting printed sheet or film is then adhered to the surface of the item (B) with or without an adhesive. The item (B) is not limited as long as it is a tangible object that can be bought and sold, and may be an artificial or natural product, or a composite of these. The material constituting the article (B) may include any of resin, metal, ceramic, and natural products, and may be, for example, paper, cloth, fiber, plastic, wood, glass, ceramic, or a composite of two or more materials selected from these.

[0117] The product (C) may be, for example, a custom-made product (C) such as clothing, footwear, daily necessities, miscellaneous goods, or stationery. Such custom-made products generally involve printing an image selected by a customer from images or samples stored on a terminal device onto the surface of a general-purpose medium, such as clothing, footwear, daily necessities, miscellaneous goods, or stationery, selected by the customer. The product (C) utilizing the present invention is preferably a custom-made product (C) in which a preferred pattern or design (A) is printed on a fabric daily necessities or miscellaneous goods (B), and more preferably a garment such as a T-shirt printed with a customer-selected pattern or design. When using the fabric item (B), the sample pattern or design (A) often looks different from the pattern or design of the actual custom-made product (C). It is difficult for customers to predict the actual finished product of such a custom-made product (C). Therefore, the image data processing method and image data processing device aim to generate an image of the product (C) that closely resembles the actual product (C) before printing.

[0118] [Image Data Processing Method / Image Data Processing] In the present invention, starting from image data (A0) corresponding to a preselected image (A) and image data (B0) of a preselected item, the following steps are carried out: ＊ ~5 ＊ Device 1 for executing ＊ ~5 ＊ By converting the image data (C0) in the above manner, image data (C0) that generates an image of the product (C) that is close to the real thing is obtained.

[0119] [Step 1 ＊ ・Device 1 ＊ Normally, when an image (A) is printed on a medium, the vividness and transparency of the original image (A) are somewhat lost depending on the printing conditions and the environment in which it is used. ＊ Then, the image data (A1) is made to approach the state of the printed image (A).

[0120] Process 1 ＊ Step 1: Image data (A0) corresponding to the image (A) is converted to image data (A1). ＊ Specifically, the image data (A1) is obtained by converting values ​​corresponding to one or more of hue, lightness, saturation, transparency, and sharpness of the image data (A0) corresponding to the image (A). The variables to be converted and the conversion amount are appropriately set depending on the characteristics of the image (A) and the type of item (B). If the item (B) is, for example, paper, cloth, wood, or the like, and the color tone of the image (A) is reproduced cloudy or dark on the actual product (C), in step 1, the image data (A1) is obtained by converting values ​​corresponding to one or more of lightness, saturation, and transparency of the image data (A0) corresponding to the image (A) so that one or more of lightness, saturation, transparency, and sharpness are reduced. Conversely, if the article (B) is, for example, plastic, glass, or the like, and the color tone of the image (A) is reproduced transparently and brightly on the actual product (C), in step 1, the image data (A1) is obtained by converting values ​​corresponding to one or more of the lightness, saturation, and transparency of the image data (A0) corresponding to the image (A) so that one or more of the lightness, saturation, transparency, and sharpness are increased.＊ The above conversion in is appropriately defined depending on the combination of the image (A) and the item (B).

[0121] If the item (B) is a custom-made T-shirt, step 1 ＊ Then, the values ​​corresponding to one or more of the brightness, saturation and transparency of the image data (A0) corresponding to the image (A) are converted so as to preferably reduce one or more of the brightness, saturation and transparency of the original image (A) to 80% or less, and more preferably reduce one or more of the brightness, saturation and transparency of the original image (A) to 75% or less, thereby obtaining the image data (A1).

[0122] Such step 1 ＊ Device 1 for performing ＊ The image data processing device generally includes a database, an image data (A0) output unit, a data conversion unit, an image data (A1) output unit, a memory, and a transmission unit. The database stores image data (A0) corresponding to selectable images (A). The device 1 outputs a signal corresponding to the image (A) input to the image data processing device. ＊ The data conversion unit extracts the requested image data (A0) from the database, converts the image data (A0) according to predefined conditions, and generates image data (A1). The generated image data (A1) is then used in step 3 described below. ＊ Device 3 for performing ＊ will be sent to.

[0123] FIG. 7 shows the process 1 ＊ and Device 1 ＊ FIG. 7 is an image diagram for understanding step 1. ＊ and Device 1 ＊ In the present invention, this corresponds to the case where image data (A1) is generated by converting the values ​​of lightness, saturation, and transparency of image data (A0) so that one or more of lightness, saturation, and transparency are reduced. ＊ ) does not need to be actually displayed, but for convenience, FIG. ＊ ), an image (101) generated from the image data (A1) ＊ ) is shown schematically. ＊) to image (A) (100 ＊ ) compared to the image (101 ＊ ) is image (A) (100 ＊ ) will appear slightly darker and murkier in color than the original.

[0124] [Step 2 ＊ ・Device 2 ＊ ] Process 2 ＊ is the same as step 1 above. ＊ This is a necessary step to reflect the appearance of the actual product (C) in the image data (A1) acquired in step (2).

[0125] Process 2 ＊ Step 2: The image data (B0) corresponding to the item (B) is converted to obtain image data (B1) corresponding to the surface state of the area of ​​the item (B) where the image (A) is to be printed. ＊ Specifically, image data (B0) corresponding to the article (B) is converted to obtain image data (B1) corresponding to the unevenness and / or texture of the area of ​​the article (B) to which the image (A) is applied. When the article (B) is made of a soft material such as paper or cloth, or a low-gloss material such as wood or ceramic, process 2 typically involves converting color image data included in the image data (B0) corresponding to the article (B) to grayscale image data and / or blurring the image data (B0) to obtain image data (B1) corresponding to the unevenness and / or texture of the area of ​​the article (B) to which the image (A) is printed. By using both conversion to grayscale image data and blurring, the flexibility, matte finish, moist feel, etc. of the surface of the article (B) can be reflected in the image data (B1). The methods for conversion to grayscale image data and blurring are appropriately determined depending on the material and shape of the article (B).

[0126] Such step 2 ＊ Device 2 for performing ＊The image processing system generally has a database, an image data (B0) output unit, a data conversion unit, an image data (B1) output unit, a memory, and a transmission unit. The database stores image data (B0). The image data (B0) includes color image data corresponding to the color tone of the item (B). The color image data includes values ​​corresponding to the bulging portions (convex portions that appear bright) on the surface of the item (B) and values ​​corresponding to the indented portions (concave portions that appear dark) on the surface of the item (B). Generally, the image data (B0) has R, G, and B values ​​assigned to each pixel as the color image data.

[0127] A distribution of color image data corresponding to the entire area of ​​the actual item (B) is generated in accordance with the wrinkles and roughness of the item (B). Based on the signal corresponding to the image (A) input to the image data processing device, the data conversion unit of the device 2 extracts from the image data (B0) a data set corresponding to the area of ​​the item (B) where the image (A) is placed, and converts the color image data included in the data set into grayscale image data according to predefined conditions. Generally, the R, G, and B values ​​included in the color image data are converted into a single grayscale value. The conversion method and the correction method associated with the conversion are appropriately determined depending on the characteristics of the image (B). Typically, grayscale data corresponding to bulging areas (bright convex areas) on the surface of the item (B) are closer to white values, and grayscale data corresponding to indented areas (dark concave areas) on the surface of the item (B) are closer to black values. In this way, image data (B1) including the grayscale image data is generated.

[0128] The data conversion unit can also perform blurring conversion on a data set corresponding to the area of ​​the article (B) on which the image (A) is placed. By performing both conversion to grayscale image data and blurring conversion, the grayscale data leaning toward white values ​​and the blackscale data leaning toward black values ​​included in the generated image data (B1) are adjusted to correspond to a state in which bulging portions (bright-appearing convex portions) and penetrating portions (dark-appearing concave portions) on the surface of the article (B) are smoothly continuous and have a fine distribution.

[0129] The blurred image data (B1) is suitable when the item (B) is made of a relatively thin fabric, such as a T-shirt. By using grayscale image data and blurring processing in combination, the image data (B1) can reflect the fine irregularities, slopes, smoothness, and texture of the fabric that are found in T-shirt fabrics, etc. ＊ and Device 2 ＊ The generated image data (A1) is then sent to the device 3 described later. ＊ will be sent to.

[0130] FIG. 8 shows the process 2 ＊ and Device 2 ＊ FIG. 8 is an image diagram for understanding step 2. ＊ and Device 2 ＊ In this case, the image data (B0) corresponding to the item (B) is converted into grayscale image data and subjected to blurring. ＊ ) does not need to be actually displayed, but for convenience, FIG. 8 shows an image (200) generated from the image data (B0) of the item (B). ＊ ), an image (201) generated from the image data (B1) ＊ ) is shown schematically. ＊ ) corresponds to the color of the item (B), while the image (201 ＊ ) is limited to the area of ​​the selected image (A) and is a black and white image.

[0131] [Step 3 ＊ ・Device 3 ＊ ] Process 3 ＊ corresponds to the first synthesis step of the image corresponding to image (A) and the image corresponding to item (B).

[0132] Process 3 ＊ Then, image data (A2) is obtained from the image data (A1) and the image data (B1). ＊Then, image data (A2) is acquired from the image data (A1) and the image data (B1), which reflects both the image (A) and the surface state of the area of ​​the article (B) to which the image (A) is applied. When a product (C) in which the image (A) is printed on the article (B) is requested, the process proceeds to step 3. ＊ Then, from the image data (A1) and the image data (B1), image data (A2) is obtained that reflects both the image (A) and the surface condition of the area of ​​the item (B) where the image (A) is to be printed.

[0133] Such step 3 ＊ Device 3 for performing ＊ The device 3 generally includes a data conversion unit, an output unit for image data (A1) and image data (B1), an output unit for image data (B1), a memory, and a transmission unit. The memory stores the image data (A1) and image data (B1). ＊ The data conversion unit synthesizes the output image data (A1) and image data (B1) by a specified program to generate image data (A2). The acquired image data (A2) is then transferred to the device 4 (described later). ＊ will be sent to.

[0134] FIG. 9 shows the process 3 ＊ and Device 3 ＊ FIG. 9 is an image diagram for understanding step 3. ＊ and Device 3 ＊ In this case, the image data (A2) is acquired from the image data (A1) and the image data (B1) to reflect both the image (A) and the surface condition of the area of ​​the article (B) where the image (A) is to be printed. In the present invention, it is not necessary to actually display the image generated by the image data (A2), but for convenience, FIG. 9 shows the image (101) generated by the image data (A1). ＊ ), an image (201) generated from the image data (B1) ＊ ), an image (301) generated from the image data (A2) ＊ ) is shown schematically. ＊ ) contains the image (101 ＊ ) Color and Image (201 ＊ ) will be visible in black and white and gray.

[0135] [Step 4 ＊ ・Device 4 ＊ ] Process 4 ＊ This corresponds to the correction process of the image data (A2) obtained in the above step 3v. When color image data is converted into grayscale data, generally, data values ​​of colors that actually appear vivid or bright may be converted into data values ​​corresponding to black or dark gray. For this reason, step 2 ＊ The grayscale data generated in step 2 does not necessarily perfectly match the brightness and darkness that can be visually recognized on the item (B).

[0136] Therefore, the above step 4 ＊ Then, the image data (A2) is converted to obtain image data (A3). ＊ Then, values ​​corresponding to one or more of the hue, brightness, saturation, transparency, and sharpness of the image data (A2) are converted to obtain the image data (A3). ＊ Then, the values ​​corresponding to one or more of the hue, brightness, saturation, transparency and sharpness of the image data (A2) are converted so that one or more of the brightness, saturation, transparency and sharpness are increased, and the image data (A3) is obtained.

[0137] Such step 4 ＊ Device 4 for performing ＊ The device 4 generally has a data conversion unit, an image data (A2) output unit, an image data (A3) output unit, a memory, and a transmission unit. The data conversion unit of the device 4 converts the output image data (A2) using a specified program to generate image data (A3). The acquired image data (A3) is transmitted to the device 5 (described later). ＊ will be sent to.

[0138] FIG. 10 shows step 4 ＊ and Device 4 ＊ FIG. 10 is an image diagram for understanding step 4. ＊ and Device 4 ＊In this case, the image data (A3) is generated by converting the values ​​corresponding to the lightness and saturation of the image data (A2) so that the lightness and saturation are increased. ＊ ) does not need to be actually displayed, but for convenience, FIG. 10 shows an image (301) generated from the image data (A2). ＊ ), an image (401) generated from the image data (A3) ＊ ) is shown schematically. ＊ ) is image (301 ＊ ) will appear brighter and more vivid than the image (301). ＊ The three-dimensional effect observed in the image (401 ＊ ) is also maintained. ＊ ) reproduces how image (A) will appear when applied (typically printed) onto article (B).

[0139] [Step 5 ＊ ・Device 5 ＊ ] Process 5 ＊ is a process for generating an image to predict a product (C) in which an image (A) is applied (typically printed) onto an article (B).

[0140] Step 5 above ＊ Then, image data (C0) corresponding to the product (C) is obtained from the image data (A3) and the image data (B0). Specifically, in step 5, image data (C0) is obtained from the image data (A3) and the image data (B0) as a sample image of a made-to-order product obtained by applying the image (A) to the item (B). When a clothing product (C) with the image (A) printed on the item (B) is requested, image data (C0) is obtained from the image data (A3) and the image data (B0) as a sample image of a made-to-order clothing item obtained by printing the image (A) on the item (B) in step 5.

[0141] Such step 5 ＊ Device 5 for performing ＊The image processing device 5 generally includes a data conversion unit, an output unit for image data (A3) and image data (B0), an output unit for image data (C0), and a memory. The data conversion unit of the device 5 synthesizes the output image data (A3) and image data (B0) using a specified program to generate image data (C0). If the acquired image data (C0) is transmitted to a display device provided separately from or connected to the image data processing device, an image generated from the image data (C0) can be displayed. The displayed image is used to provide a sample of the product (C) for the manufacture and sale of the product (C).

[0142] FIG. 11 shows the process 5 ＊ and Device 5 ＊ FIG. 11 shows an image diagram for understanding the process. FIG. 11 shows a case where the present invention is used to manufacture and sell a custom-made shirt (C) obtained by printing an image (A) on an item (B). ＊ and Device 5 ＊ In this case, the image data (A3) and the image data (B0) are combined to obtain image data (C0) as a sample image. ＊ ) is generated. ＊ ) is treated as a sample image of the above custom-made shirt (C).

[0143] FIG. 12 shows the image (501 ＊ ) is an image diagram for understanding that it is effective as a sample image. Figure 12 shows the display of a sales site for custom-made T-shirts obtained by printing image (A) on item (B). ＊ In the case of (11), a customer looks at image (A) and image (B0) of item (B), imagines the finished shirt, and places an order. It is difficult to accurately predict the texture and color tone of the shirt that will actually be delivered just by looking at image (A) and image (B0). In contrast, when a sample image of a custom-made T-shirt is displayed using the image data processing method and image data processing device described above (11), ＊) allows relatively accurate prediction of the texture and color tone of the finished T-shirt. By providing such sample images, it is possible to increase customer purchasing motivation and product satisfaction.

[0144] 13 is a diagram showing the process flow of the image data processing and the image data processing device. ＊ ~5 ＊ was carried out.

[0145] 14 shows an image generated from "Data B1" acquired by Step 2 and Device 2 shown in FIG. 13. In this image, the color tone of the original T-shirt has changed to black and white / gray generated by the grayscale data.

[0146] Figure 15 shows an image generated by "Data A3" shown in Figure 13. However, in the specification, an actual color image is displayed in black and white mode. This image reproduces the color landscape photograph printed on the finished T-shirt. This image also expresses shadows caused by wrinkles and curvatures in the T-shirt. "Data C" is obtained by combining this image with the T-shirt image to display a sample image of a custom-made T-shirt. The sample image allows customers and manufacturers to predict with a high degree of accuracy the printed area on the finished T-shirt.

[0147] [Virtual Shop] The virtual shop (100) of the present invention provides services to the above-mentioned users (excluding the "users" and "users" used in the explanations of References 1 and 2) as potential customers. The virtual shop (100) comprises an image generation device (1). The virtual shop (100) is provided by a virtual shop operation method (110) that uses an image generation method (10).

[0148] The virtual shop (100) receives a designation of a work (21) from a user in a work data input unit (2), determines goods (41) to be proposed to the user in a product selection unit (3), and provides a goods image (42) to the user in a goods image generation unit (4). The virtual shop operating method (110) registers the work (21) designated by the user in a work data input step (20), determines goods (41) to be proposed to the user in a product selection step (30), and provides a goods image (42) to the user in a goods image generation step (40).

[0149] When the user accepts and adopts the goods (41), the virtual store (100) can order the manufacture of the goods (41) and deliver the actual goods (41) to the user. In this case, the user can send the quantity of the goods (41), delivery information, and payment information to the virtual store (100) from their terminal device and order the goods (41).

[0150] The virtual shop (100) may have functions adopted in known e-commerce sites in addition to the work data input unit (2), item selection unit (3), and goods image generation unit (4). The virtual shop (100) may be equipped with, for example, an accounting unit, a quality control unit, and a customer communication unit. The accounting unit can collect payment for goods (41) in conjunction with payment systems such as banks, credit cards, and electronic money, and can register and update the user's payment status. The quality control unit and customer communication unit can collect and analyze user feedback on the goods (41).

[0151] Furthermore, the user can also sell the goods (41) he or she has received by setting up a shop in the virtual store (100). In this case, the virtual store (100) not only provides the user with the service of proposing goods "41" to the user, but also sells the goods (41) to other users, typically to an unspecified number of users who can view the goods (41) via the Internet, thereby earning a sales commission. The transaction method for the completed goods (41) in the virtual store (100) can be appropriately selected from the methods used in well-known so-called online stores and Internet stores.

[0152] In this case, the user can start from a vague desire to "sell some goods by himself" and move on to commercializing and merchandizing his own creations through the virtual store (100).

[0153] An embodiment of the present invention will be described below with reference to Fig. 18. The drawings contain exaggerations and omissions and do not accurately represent the actual appearance. The present invention is not limited to the examples shown in the drawings.

[0154] [Example] Figure 18 shows how a user uses a virtual shop (100). The image generation device (1) and image generation method (10) operate and are executed by an application placed on a server on the Internet. The user is the owner of a cat cafe. The user wants to sell merchandise with photos of cats to foster parents, and use the proceeds to fund the operation of the cat cafe. To do this, the user takes a photo (21) of Cat A, a popular cat at the cat cafe, and saves the image data (22) as artwork data.

[0155] First, the application executes a work data input step (20), and the work data input unit (2) of the image generating device (1) receives work data (22) from the user's smartphone (5) connected to the Internet (6). The work data input unit (2) registers the received work data (22). The user further transmits data related to the video of Cat A that was distributed over the Internet and information about the cat cafe as user data (23) to the work data input unit (2).

[0156] Next, the application executed an item selection step (30). Here, the item selection unit (3) analyzed the artwork data (22), cropped the image around Cat A's face, and further generated text data related to the appearance of the artwork (21): "round face." Furthermore, the item selection unit (3) analyzed the user data (23) and generated text data related to Cat A and the evaluation of the cat cafe: "at my own pace," "bag," "cafe au lait," and "plenty." The item selection unit (3) used this text data to search the online sales site of Department Store B, which operates the virtual shop (100), and evaluated the affinity with the collected product information using a predetermined prediction model. As a result, the item selection unit (3) extracted a shopping bag (311, not shown) large enough to fit Cat A and a large mug (312, not shown) as items (31) recommended to the user. The product selection unit (3) registered the shopping bag image data (321) and the Magcack image data (322) in association with the artwork data (22).

[0157] The application then executes a goods image generation step (40), and the goods image generation unit (4) generates a goods image (421) by combining the artwork data (22) with the image data (321) of the shopping bag (311), and a goods image (422) by combining the artwork data (22) with the image data (322) of the mug (312). The goods image (421) also reflects the shading caused by the unevenness of the surface of the shopping bag. The goods image (422) reflects the gloss and shading caused by the curvature of the mug pack.

[0158] On smartphone (5), the user first viewed merchandise image (421), then switched screens to view merchandise image (422). The user knew that other cat cafes sold postcards with cat photos printed on them, but shopping bags and mugs were unexpected. However, the user came to believe that the shopping bags and mugs would be effective because, if cat-loving customers could use them, they might be reminded of Cat A while out or at home and be motivated to return to the cat cafe, and because they would also serve as advertising for the cat cafe to customers' families and friends.

[0159] After that, the user decided to sell a shopping bag (411, not shown) with a photo of Cat A and a mug (412, not shown) with a photo of Cat A at the cat cafe store, and placed an order for production with the virtual store (100). The virtual store (100) arranged for the "sale of personalized shopping bags (311) and mugs (312)" through the online sales system of department store B, and delivered the shopping bag (411) and mug (412) with Cat A's face to the user as goods (41).

[0160] This invention allows even consumers and creators with little knowledge or experience in creating and selling goods to develop highly satisfying goods in a short period of time. As a result, it is expected that high-value-added products will be introduced to the market, revitalizing the goods market.

[0161] 1' image information processing device 2' information input unit 3' information output unit 4' information transmission / reception unit 5' first layer creation processing unit 6' second layer creation processing unit 7' color correction layer creation processing unit 8' combined layer creation processing unit 9' transparency correction layer creation processing unit 10' post-processing template creation processing unit 11' post-combination image information creation processing unit 20' storage unit 1 ＊ Process 1 ＊ , device 1 ＊ 100 ＊ Image (A) 101 ＊ Image generated from image data (A1) 2 ＊ Process 2 ＊ , device 2 ＊ 200 ＊ Image 201 generated from image data (B0) ＊ Image generated from image data (B1) 3 ＊ Process 3 ＊ , device 3 ＊ 301 ＊ Image generated from image data (A2) 4 ＊ Process 4 ＊ , device 4 ＊ 401 ＊ Image generated from image data (A3) 5 ＊Process 5 ＊ , device 5 ＊ 501 ＊ Image generated by image data (C0) 10 ＊ Sample display 11 without using the image data processing method and image data processing device ＊ REFERENCE SIGNS LIST Sample display using the image data processing method and image data processing device 1 Image generation device 2 Work data input unit 20 Work data input step 21 Work 21 Work (photo of cat) 22 Work data (photo data) 23 User data 231 User registration information 3 Item selection unit 30 Item selection step 31 Item 32 Item data 321 Item (shopping bag) data 322 Item (mug) data 4 Goods image generation unit 40 Goods image generation step 41 Goods 42 Goods image 421 Goods (eco bag) image 422 Goods (mug) image 5 Smartphone 6 Internet 7 Reference image

Claims

1. An image generation device (1) having a work data input unit (2), an item selection unit (3), and a goods image generation unit (4), wherein the work data input unit (2) acquires image data (work data) (22) of a work (21) designated by a user and, optionally, data related to the user (user data) (23), the item selection unit (3) automatically selects an item (31) to which the work (21) is to be attached based on the work data (22) and the arbitrary user data (23), and acquires image data (item data) (32) of the item (31), and the goods image generation unit (4) uses the work data (22) and the item data (32) to generate an image (goods image) (42) of goods (41) in which the work (21) is attached to the item (31).

2. The image generating device (1) according to claim 1, wherein in the work data input section (2), the work data (22) is image data of a two-dimensional work (21).

3. The image generating device (1) according to claim 1, wherein the object selection unit (3) uses an artificial intelligence (33) to select the object (31).

4. The image generating device (1) according to claim 1, wherein the goods image generating unit (4) generates one or more goods images (42) for each of one or more goods (41).

5. An image generation method (10) comprising a work data input step (20), an item selection step (30), and a goods image generation step (40), wherein in the work data input step (20), image data (work data) (22) of a work (21) designated by a user and, optionally, data related to the user (user data) (23) are acquired, in the item selection step (30), an item (31) to which the work (21) is to be attached is automatically selected based on the work data (22) and any user data (23), and image data (item data) (32) of the item (31) is acquired, and in the goods image generation step (40), an image (goods image) (42) of goods (41) in which the work (21) is attached to the item (31) is generated using the work data (22) and the item data (32).

6. The image generating method (1) according to claim 5, wherein in the work data input step (20), the work data (202) is image data of a two-dimensional work (201).

7. The image generating method (10) according to claim 5, wherein in the item selection step (30), an artificial intelligence (303) selects the item (301).

8. The image generating method (10) according to claim 5, wherein in the item image generating step (40), one or more item images (402) are generated for each of one or more items (401).

Images