Image processing device, image processing method, and program

Abstract

An image processing device for acquiring, with regard to a human object located in a virtual space, map data of a human object surface that is used in order to determine the external appearance of a region of the human object that corresponds to the skin, acquiring state information that indicates the state in the virtual space, and correcting a value included in the map data on the basis of the acquired state information, wherein a spatial image showing the status of the virtual space is drawn by the image processing device using the corrected map data.

Description

[Technical Field]

[0001] The present invention relates to an image processing device, an image processing method, and a program for executing processing related to drawing an image including human skin. [Background technology]

[0002] In the field of 3D computer graphics, various efforts have been made to render highly realistic images, which has made it possible in recent years to render images that are closer to life-like images. Summary of the Invention [Problem to be solved by the invention]

[0003] In the above-mentioned conventional technology, it is important to represent human skin with a more realistic appearance. However, the condition of real human skin changes due to various factors such as the surrounding environment. Therefore, simply improving the image quality is not enough to depict human skin with a more realistic appearance.

[0004] The present invention has been made in consideration of the above-mentioned circumstances, and one of its objectives is to provide an image processing device, an image processing method, and a program that can draw images including human skin with higher realism. [Means for solving the problem]

[0005] An image processing device according to one aspect of the present invention includes an object data acquisition unit that acquires map data of the surface of a human object placed in a virtual space, which is used to determine the appearance of an area corresponding to the skin of the human object; a state information acquisition unit that acquires state information indicating the state within the virtual space; and a correction unit that corrects values ​​included in the map data based on the state information, and is characterized in that a spatial image showing the appearance of the virtual space is drawn using the corrected map data. An image processing method according to one aspect of the present invention includes an object data acquisition step of acquiring map data of the surface of a human object placed in a virtual space, the map data being used to determine the appearance of an area corresponding to the skin of the human object; a state information acquisition step of acquiring state information indicating a state within the virtual space; and a correction step of correcting values ​​contained in the map data based on the state information, wherein the corrected map data is used to draw a spatial image showing the appearance of the virtual space. A program according to one aspect of the present invention causes a computer to execute the following steps: an object data acquisition step of acquiring map data of a surface of a human object to be placed in a virtual space, the map data being used to determine the appearance of a region corresponding to the skin of the human object; a state information acquisition step of acquiring state information indicating a state of the virtual space; and a correction step of correcting values ​​included in the map data based on the state information, wherein the corrected map data is used to draw a spatial image showing an appearance of the virtual space. This program may be provided by being stored in a computer-readable, non-transitory information storage medium. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a block diagram showing a configuration of an image processing device according to an embodiment of the present invention; [Figure 2] 1 is a functional block diagram showing functions of an image processing device according to an embodiment of the present invention; [Figure 3] FIG. 1 is a diagram showing an example of a sebum secretion map of a human face. [Figure 4] 10 is a graph showing an example of a change in correction value over time. [Figure 5] FIG. 2 is a flowchart showing an example of a flow of processing executed by an image processing device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0008] 1 is a block diagram showing the configuration of an image processing device 1 according to one embodiment of the present invention. The image processing device 1 is, for example, a home game console, a portable game console, a personal computer, a smartphone, a tablet, etc., and as shown in the figure, is configured to include a control unit 11, a storage unit 12, and an interface unit 13. The image processing device 1 is also connected to a display device 14 and an operation device 15.

[0009] The control unit 11 includes at least one processor such as a CPU, and performs various information processing by executing programs stored in the storage unit 12. Specific examples of the processing performed by the control unit 11 in this embodiment will be described later. The storage unit 12 includes at least one memory device such as a RAM, and stores the programs executed by the control unit 11 and data processed by the programs.

[0010] The interface unit 13 is an interface for data communication between the display device 14 and the operation device 15. The image processing device 1 is connected to the display device 14 and the operation device 15 via the interface unit 13 either wired or wirelessly. Specifically, the interface unit 13 includes a multimedia interface for transmitting video data supplied by the image processing device 1 to the display device 14. It also includes a data communication interface for receiving signals indicating the content of operations performed by the user on the operation device 15.

[0011] The display device 14 is a liquid crystal display, an organic EL display, or the like, and displays an image on a screen according to a video signal supplied from the image processing device 1. The display device 14 may be a head-mounted display device that presents an image to each of the user's left and right eyes. The operation device 15 is, for example, a keyboard, a mouse, a controller for a home game console, or the like, and accepts operation input from the user. Note that both the display device 14 and the operation device 15 may be built into the housing of the image processing device 1, or may be separate devices connected to the image processing device 1 by wire or wirelessly. The operation device 15 may include push buttons or a touch panel arranged on the surface of the housing of the image processing device 1.

[0012] Functions realized by the image processing device 1 will be described below with reference to the functional block diagram of FIG. 2. In this embodiment, the image processing device 1 executes processing for drawing a spatial image showing the state of a virtual three-dimensional space (virtual space). As shown in FIG. 2, the image processing device 1 functionally includes an object data acquisition unit 21, a state information acquisition unit 22, a layer map correction unit 23, and a spatial image drawing unit 24. These functions are realized by the control unit 11 operating in accordance with a program stored in the storage unit 12. This program may be provided to the image processing device 1 via a communication network such as the Internet, or may be provided by being stored in a computer-readable information storage medium such as an optical disc.

[0013] The object data acquisition unit 21 acquires object data necessary for drawing three-dimensional objects to be placed in the virtual space. In particular, in this embodiment, the objects to be drawn by the image processing device 1 include at least a human object representing a person. This human object may be a user object that is operated by a user of the image processing device 1. In this case, the position, orientation, posture, etc. of the user object in the virtual space will change depending on the content of the operation input by the user to the operation device 15.

[0014] The data for each object includes shape data that defines the general shape of the object's three-dimensional model and appearance data for determining the appearance of the object's surface. In particular, the appearance data for a human object includes data for multiple types of layer maps L that cover at least the area corresponding to the person's skin. Each layer map L corresponds to an overlapping area on the human object's surface and is used to determine the appearance of the corresponding area. Each layer map L has a data structure similar to that of a two-dimensional image, with one or more values ​​(hereinafter referred to as pixel values ​​for convenience) assigned to each of multiple pixels arranged along a two-dimensional coordinate system (e.g., a UV coordinate system). Each pixel included in the layer map L corresponds to a specific position on the human object's surface.

[0015] Specifically, the layer map L of the human object may include a color map indicating the reference color of the human object surface, a height map indicating the elevation of the surface, a roughness map indicating the roughness of the surface, a sebum secretion amount map indicating the degree of sebum secretion of the skin, a moisture content map indicating the degree of moistness or hydration of the skin, etc. The spatial image rendering unit 24, which will be described later, performs a mapping process in which these multiple layer maps L are overlaid and attached to the surface of the three-dimensional model, thereby determining the color and texture to be used to render the skin on the surface of the human object.

[0016] Figure 3 shows an example of a sebum secretion map for a human face. This example shows the contents of the sebum secretion map pasted onto a 3D model of a human face, with the shade of color indicating the degree of sebum secretion. This sebum secretion map shows areas of the human face where sebum is easily secreted and areas where it is not. When drawing a human object, the light transmittance of the corresponding position on the face surface is determined based on the pixel value of each pixel included in this sebum secretion map, making it possible to express the oily shine of the skin caused by sebum secretion.

[0017] The state information acquisition unit 22 acquires information about the state in the virtual space that may affect the appearance of human skin. Hereinafter, information about the state in the virtual space acquired by the state information acquisition unit 22 is referred to as state information. The state information may include environmental information about the environment of the virtual space, such as the temperature (air temperature) and humidity within the space set for the virtual space. It may also include information about the state of the human object itself that is to be drawn. Specific examples of state information will be described later. Note that the state information may be information that represents a state that changes over time in the virtual space. In this case, the state information acquisition unit 22 periodically acquires information that represents the current state.

[0018] The layer map correction unit 23 corrects each layer map L of the human object acquired by the object data acquisition unit 21 using the status information acquired by the status information acquisition unit 22. For example, when the temperature in the virtual space is high, the amount of sweat and sebum secreted by the person increases, and it is expected that the influence of sweat and sebum will be greater than the values ​​defined in the pre-prepared sebum secretion amount map and moisture amount map. Therefore, the layer map correction unit 23 corrects the pixel value of each pixel included in the layer map L used to determine the appearance of the human object so as to reflect the influence of the virtual space environment and the person's status indicated by the status information. This makes it possible to express skin conditions that have changed from the contents of the pre-prepared layer map L due to the influence of the virtual space environment and the person's status.

[0019] Specifically, the layer map correction unit 23 calculates the correction value using a calculation model M prepared in advance. This calculation model M is a model that defines a function that receives the value of the state information acquired by the state information acquisition unit 22 as input and outputs a correction value to be applied to the corresponding layer map L. The calculation model M may be a simple calculation formula such as a linear function, or a more complex function. Furthermore, as will be described later, the calculation model M may be a calculator generated by machine learning.

[0020] A calculation model M is prepared for each layer map L to which the correction is to be applied. For example, when performing correction on three types of layer maps L, a separate calculation model M is prepared for each of the three types of layer maps L. By inputting the same state information value for these three types of layer maps L, three correction values ​​are obtained. Note that it is not necessarily necessary to correct all layer maps L used to draw a spatial image based on a certain type of state information; correction values ​​are calculated for some or all of the layer maps L that are predetermined, depending on the type of state information.

[0021] After calculating the correction value, the layer map correction unit 23 uses the calculated correction value to correct the pixel value of each pixel included in the corresponding layer map L. For example, the layer map correction unit 23 corrects the pixel value by multiplying the pixel value of each pixel by the correction value. Alternatively, the layer map correction unit 23 may add the correction value to the pixel value of each pixel, or may change the pixel value using a more complex calculation formula. Furthermore, if the correction results in a pixel value that exceeds a predetermined numerical range, the corrected numerical value may be converted to an upper or lower limit so that it falls within the numerical range of the pixel value.

[0022] The calculation model M may accept values ​​of multiple types of state information as input. For example, if a correction is desired that takes into account the effects of both temperature and humidity in the virtual space, the calculation model M inputs the values ​​of these two types of environmental information and calculates one correction value to be applied to the corresponding layer map L. In this case, the calculation model M is a function with two input variables. Alternatively, a separate calculation model M may be prepared for each type of state information. In this case, a correction value for the layer map L is calculated independently according to each type of state information. The layer map correction unit 23 then corrects the layer map L according to the multiple types of state information by redundantly applying the multiple correction values ​​calculated according to the multiple types of state information to the pixel values ​​in the corresponding layer map L.

[0023] Furthermore, the correction value calculated by the calculation model M may be a value that is applied only to a specific type of pixel value among the pixel values ​​included in the target layer map L. For example, if it is expected that the redness of skin will increase under a specific environment, a correction value that increases the red pixel value among the RGB pixel values ​​included in the color map may be calculated.

[0024] The spatial image rendering unit 24 renders a two-dimensional spatial image showing the state of the virtual space using the object data acquired by the object data acquisition unit 21. At this time, the spatial image rendering unit 24 performs a mapping process for the human object by overlaying multiple layer maps L corrected by the layer map correction unit 23. Then, the spatial image rendering unit 24 renders a spatial image showing the virtual space including the human object mapped by the layer map L as viewed from a given viewpoint. Note that the mapping process using the corrected layer map L and the spatial image rendering process itself may be realized by known methods. The image processing device 1 may display the rendered spatial image on the screen of the display device 14, or may distribute it to another device via a communication network. Alternatively, the rendered spatial image may be stored in a storage device such as a hard disk drive.

[0025] A specific example of the process of correcting the layer map L using the state information will be described below.

[0026] As an example, the state information acquisition unit 22 acquires the temperature value of the virtual space as part of the environmental information. The layer map correction unit 23 calculates a correction value for the sebum secretion map by inputting the acquired temperature value into a pre-prepared calculation formula. Then, correction according to the temperature of the virtual space is performed by multiplying the pixel value (a value indicating the degree of sebum secretion) of each pixel in the sebum secretion map to be corrected by the calculated correction value. The space image drawing unit 24 draws the appearance of the human object by compositing the corrected sebum secretion map with another layer map L and performing mapping processing. This makes it possible to express the appearance of shining skin due to increased sebum secretion in a high-temperature environment.

[0027] Furthermore, environmental effects on the skin generally do not appear immediately but rather appear over time. Therefore, it is desirable that the correction value for each layer map L be determined not only based on the value of state information such as temperature itself, but also on the length of time that the state has continued. Therefore, the state information acquisition unit 22 may acquire, as one piece of state information, information indicating the elapsed time since a specific state began, and the layer map correction unit 23 may calculate the correction value using this time information.

[0028] FIG. 4 is a graph showing an example of changes in correction values ​​over time. The graph in this figure shows an example of correction values ​​when a certain temperature value continues. In this example graph, the calculation model M that calculates the correction values ​​is defined as a linear function of elapsed time. Also, in this graph, three correction values, namely, a correction value for the height map (a in the figure), a correction value for the roughness map (b in the figure), and a correction value for the sebum secretion amount map (c in the figure), are calculated using different calculation models M. This graph shows that the influence on the roughness map and sebum secretion amount map becomes stronger as time passes. Note that this graph shows a graph when time passes under a specific temperature environment, and correction values ​​may be calculated under different temperature environments using different linear functions, for example, with different intercepts or slopes.

[0029] For example, in games, the time required to complete the same scene may vary from user to user. The image processing device 1 according to this embodiment can change the skin condition according to the elapsed time. This allows a user who can complete a particular scene in a short time to be less affected by the environment of that scene, while a user who takes longer to complete the scene can have a human object gradually sweat due to the environment. The layer map correction unit 23 may perform corrections using only the elapsed time of a particular scene as status information, regardless of factors such as temperature. This allows for the representation of changes in skin due to factors such as fatigue as a particular scene continues.

[0030] The effect of the temperature in the virtual space may also appear in layer maps L of types other than the layer map L shown in FIG. 4. For example, when the temperature is high, sweat is secreted, increasing the amount of moisture in areas of the surface of a human object where sweating is likely to occur. Also, in environments where the temperature is lower than normal, such as on snowy mountains, changes in properties that differ from those at high temperatures occur, such as reddening of the skin. Therefore, a separate layer map L that represents the location and degree of redness that occurs on the skin may be prepared, and a correction value for this layer map L when the temperature is low may be calculated using a separate calculation model M.

[0031] Furthermore, the layer map correction unit 23 may correct the layer map L based on various environmental information set for the virtual space, such as humidity in addition to temperature. Furthermore, the correction value for the layer map L to be mapped to the target human object may be determined based not only on environmental information indicating the environment of the entire virtual space but also on status information indicating the status of the target human object. For example, if a person in the virtual space is intoxicated or sunburned, their skin may change, such as becoming reddish. In this case, the skin change is not always constant, and the degree of change is thought to vary depending on the degree of intoxication or sunburn. Therefore, the image processing device 1 may acquire information indicating the degree of intoxication or sunburn as part of the status information, and calculate a correction value for the layer map L that defines the skin change caused by intoxication or sunburn based on that value.

[0032] Furthermore, one of the changes that may occur on the skin of a human object is a mark caused by a blow or the like. The location where such a mark appears is not fixed, but the nature of the change itself may be defined by the layer map L. In this case, the image processing device 1 may also acquire status information indicating the strength of the blow or the like that the person has received, and calculate a correction value for the layer map L that defines the mark caused by the blow or the like based on that value. This makes it possible to make a more distinct (darker) mark appear on the skin when a strong blow has been received.

[0033] An example of a method for determining the calculation model M used to calculate the correction value will be described below. In the above example, a simple linear function of elapsed time is used as the calculation model M to calculate the correction value, but the effects of actual temperature, humidity, and the like on the skin condition may appear in more complex forms. Therefore, in this embodiment, the correction value may be calculated using a calculation model M generated by machine learning or the like.

[0034] In this example, images are captured of the changes in the skin condition of a real person over time in various environments. For example, a person is photographed in a specific temperature environment at regular intervals, starting from the time the person was first placed in that environment. Similarly, images of the same person's face are captured at regular intervals in a different temperature environment. By collecting a large number of such sample data, it is possible to analyze how changes occur in a person's face over time and with temperature. This photographing is not limited to optical photographing of two-dimensional images, but may also include photographing from various perspectives, such as photographing three-dimensional images or photographing temperature distribution using a thermal camera.

[0035] As described above, the image processing device 1 analyzes images captured under various conditions to generate multiple types of layer maps representing a person's face. Specifically, if the captured image is a two-dimensional image, the image's contents are analyzed to identify the three-dimensional shape of the face and converted into a map image such as a UV coordinate system. Furthermore, by analyzing the color distribution and temperature distribution contained in the map image, layer maps capable of reproducing the captured face, such as a color map, height map, and sebum secretion map, are generated.

[0036] Thereafter, the image processing device 1 performs machine learning using the generated layer map and state information indicating the state (here, temperature and elapsed time) when the image used to generate the layer map was captured as training data, thereby generating a calculation model M for outputting correction values ​​indicating the degree of change that appears in a person's face due to differences in temperature and the passage of time.

[0037] The image processing device 1 may perform this type of analysis not only on the face but also on other parts such as the hands and feet, thereby determining the calculation model M for correction values ​​for each part of the human body. Alternatively, the calculation model M for correction values ​​obtained as a result of analyzing a specific part may be used to determine correction values ​​for other parts. As described above, the extent to which sebum secretion, sweat, and the like appear on which parts of human skin is defined by a separate layer map L. Assuming that the influence of the environment itself appears in a similar manner on the entire human skin, the calculation model M for correction values ​​to be applied to the layer map L may be used commonly for the skin of the entire body.

[0038] Furthermore, changes in the condition of a person's skin vary from person to person. Specifically, the tendency for people to sweat easily, secrete sebum, and get sunburned tends to differ depending on their attributes, such as age, gender, and race. Furthermore, even among people with the same attributes, there may be differences due to individual characteristics, such as some people sweating easily and others not. Therefore, it is possible to prepare individual calculation models M in advance according to the attributes and characteristics of the target person, and select the calculation model M to be applied to each human object to be drawn.

[0039] As a specific example, the image processing device 1 performs the above-mentioned machine learning individually for people with different attributes, and generates calculation models M independently of each other. That is, for each attribute, such as men in their 20s and women in their 30s, multiple people belonging to that attribute are photographed as samples to generate training data. By performing machine learning using the training data obtained in this way as input, it is possible to generate a calculation model M that reflects the skin condition of people belonging to each attribute.

[0040] Furthermore, for human characteristics such as people who sweat easily or not, machine learning may be performed independently using people with each characteristic as samples to generate individual computational models M. Alternatively, the computational model M obtained by performing machine learning on people with various characteristics may be modified, such as by correcting the output value, to calculate corrected values ​​for people with different characteristics.

[0041] When different corrections are to be made depending on the attributes and characteristics of a person, the state information acquisition unit 22 acquires information specifying the attributes and / or characteristics of the target human object along with the state information. Then, the layer map correction unit 23 selects a calculation model M to be used from a plurality of calculation models M prepared in advance according to the specified attributes and / or characteristics, and inputs the values ​​of the state information (e.g., temperature, elapsed time, etc.) into the selected calculation model M. This makes it possible to calculate a correction value that reflects the attributes and characteristics of the person. Note that when multiple human objects exist in the virtual space, attribute and characteristic information is acquired for each person, and a correction value for the layer map L is calculated for each person. This makes it possible to make changes that occur in the skin different for different people even in the same environment.

[0042] In the above description, the image processing device 1 itself performs machine learning to generate the calculation model M, but the machine learning itself may be performed by another information processing device. In this case, the image processing device 1 stores data of the calculation model M generated by the other information processing device and uses the data as needed when drawing a spatial image.

[0043] An example of the flow of processing executed by the image processing device 1 according to this embodiment when rendering a moving image will be described below with reference to the flowchart of FIG.

[0044] First, the object data acquisition unit 21 acquires object data such as shape data, appearance data, position in the virtual space, and information specifying orientation for each of a plurality of objects present in the virtual space (S1). After that, the state information acquisition unit 22 acquires state information of the virtual space at the time of rendering (S2).

[0045] Next, the layer map correction unit 23 inputs the state information acquired in S2 into the corresponding calculation model M for each layer map L to be corrected, and calculates a correction value (S3).Then, the layer map correction unit 23 corrects the multiple layer maps L included in the appearance data specified in S1 using the calculated correction value (S4).

[0046] Thereafter, the spatial image rendering unit 24 determines the appearance of the human object by mapping the multiple layer maps L corrected in S4 onto a three-dimensional model defined by the shape data acquired in S1 (S5). Then, a spatial image showing the state of this virtual space is rendered and written to the frame buffer memory in the storage unit 12 (S6). The spatial image written to the frame buffer memory is displayed on the screen of the display device 14 as a frame image.

[0047] The image processing device 1 repeatedly executes the above-described processing at a predetermined frame rate, thereby generating a moving image showing the change over time in the virtual space, and can display the moving image on the screen of the display device 14.

[0048] The above-described processing may be realized by, for example, a game engine, and the game engine may also provide a calculation model M for calculating correction values ​​according to state information. In this case, the game application program specifies to the game engine shape data and appearance data of objects to be drawn, as well as state information of the virtual space for each frame. In this way, the game application itself does not need to consider how the temperature, humidity, etc. of the virtual space affect the appearance of a person. Simply by specifying temperature and humidity information to the game engine, the game application can present the appearance of a person that reflects that environment to the game player.

[0049] Alternatively, the calculation model M may be provided by the game application program. In this case, the game engine calculates correction values ​​using the specified calculation model M and performs corrections on the layer map L specified by the game. In this way, it is possible to present to the player how the appearance of human skin changes in a manner suited to the content of the game.

[0050] Furthermore, at least some of the functions described above may be executed by the application program itself. For example, the application program may calculate correction values ​​based on the state of the virtual space and the attributes of the person to be drawn, and specify these values ​​together with the layer map L to be used to the game engine. In this case, the game engine corrects the layer map L using the specified correction values ​​and draws the spatial image using the corrected layer map L.

[0051] Furthermore, the spatial images rendered by the image processing device 1 according to the embodiment of the present invention are not limited to game images that change in real time. For example, even when generating pre-rendered images whose content does not change in response to user operations, by specifying in advance state information that indicates the state of the virtual space at each scene or elapsed time, it is possible to generate, with relatively little effort, images in which the appearance of human skin changes in response to that state.

[0052] As described above, the image processing device 1 according to this embodiment uses a layer map L that has been corrected in consideration of the state in the virtual space, making it possible to render a spatial image that expresses the appearance of human skin in the virtual space with greater realism. Furthermore, by correcting the layer map L using a dynamically calculated correction value, it is no longer necessary to prepare a large number of layer maps L in advance to express various states, and it becomes possible to express various skin states relatively easily.

[0053] It should be noted that the embodiments of the present invention are not limited to those described above. For example, the status information acquired by the status information acquisition unit 22 in the above description is merely an example, and various other status information that may affect the status of a person's skin may also be acquired. Furthermore, the layer map L that is the target of correction according to the status information is not limited to the above example, and may be various types used to determine the appearance of a person's skin. [Explanation of symbols]

[0054] 1 image processing device, 11 control unit, 12 storage unit, 13 interface unit, 14 display device, 15 operation device, 21 object data acquisition unit, 22 state information acquisition unit, 23 layer map correction unit, 24 space image drawing unit.

Claims

1. an object data acquisition unit that acquires map data of a surface of a human object placed in a virtual space, the map data being used to determine the appearance of a region corresponding to the skin of the human object; a state information acquisition unit that acquires state information indicating a state in the virtual space; a correction unit that calculates a correction value according to the state information using one of a plurality of calculation models prepared in advance and corrects values ​​included in the map data using the calculated correction value; Including, the state information acquisition unit acquires, together with the state information, information specifying a characteristic of the person object; the correction unit inputs the state information into a calculation model selected from the plurality of calculation models in accordance with the specified characteristics, calculates a correction value in accordance with the state information, and corrects a value included in the map data using the calculated correction value; A space image showing the state of the virtual space is drawn using the corrected map data.

1. An image processing device comprising:

2. 2. The image processing device according to claim 1, each of the plurality of computational models is generated by machine learning using map data obtained by photographing an actual person having predetermined characteristics as training data; The correction unit calculates a correction value according to the state information using a calculation model generated using map data obtained by photographing a real person having characteristics corresponding to the characteristics of the specified human object.

1. An image processing device comprising:

3. 3. The image processing device according to claim 1, the object data acquisition unit acquires a plurality of types of map data; The correction unit calculates a different correction value for each of the plurality of types of map data, and corrects a value included in the corresponding map data using the calculated correction value.

1. An image processing device comprising:

4. 4. The image processing device according to claim 1, The state information includes environment information that indicates the environment of the virtual space.

1. An image processing device comprising:

5. 5. The image processing device according to claim 4, The environmental information includes either the temperature or the humidity in the virtual space.

1. An image processing device comprising:

6. 6. The image processing device according to claim 1, the status information includes information indicating a status of the human object itself; The information indicating the state of the human object itself is information indicating the degree of drunkenness or the degree of sunburn.

1. An image processing device comprising:

7. 7. The image processing device according to claim 1, the state information includes elapsed time information indicating a duration of the state, Changing the appearance of the region corresponding to the skin of the human object based on the elapsed time information.

1. An image processing device comprising:

8. an object data acquisition step of acquiring map data of a surface of a human object placed in a virtual space, the map data being used to determine the appearance of a region corresponding to the skin of the human object; a state information acquisition step of acquiring state information indicating a state in the virtual space; a correction step of calculating a correction value according to the state information using one of a plurality of calculation models prepared in advance, and correcting a value included in the map data using the calculated correction value; Including, In the state information acquisition step, information specifying a characteristic of the person object is acquired together with the state information; In the correction step, the state information is input to a calculation model selected from the plurality of calculation models according to the specified characteristics, a correction value according to the state information is calculated, and a value included in the map data is corrected using the calculated correction value; A space image showing the state of the virtual space is drawn using the corrected map data. An image processing method comprising:

9. an object data acquisition step of acquiring map data of a surface of a human object placed in a virtual space, the map data being used to determine the appearance of a region corresponding to the skin of the human object; a state information acquisition step of acquiring state information indicating a state in the virtual space; a correction step of calculating a correction value according to the state information using one of a plurality of calculation models prepared in advance, and correcting a value included in the map data using the calculated correction value; A program for causing a computer to execute the above, In the state information acquisition step, information specifying a characteristic of the person object is acquired together with the state information; In the correction step, the state information is input to a calculation model selected from the plurality of calculation models according to the specified characteristics, a correction value according to the state information is calculated, and a value included in the map data is corrected using the calculated correction value; A space image showing the state of the virtual space is drawn using the corrected map data. program.

Images