Biological model generation device, biological model generation program, and biological model generation method

Abstract

A biological model generation device (100) is provided with: a parameter acquisition unit (21) that acquires a value of a first parameter relating to the outer shape of a specific living body; an estimation unit (22) that, on the basis of statistical data relating to the outer shapes of a plurality of living bodies of the same type as the specific living body and the value of the first parameter acquired by the parameter acquisition unit, calculates an estimated value of a second parameter indicating the outer shape of a living body corresponding to the value of the first parameter; and a biological model generation unit (30) that, on the basis of the value of the first parameter acquired by the parameter acquisition unit (21) and the estimated value of the second parameter calculated by the estimation unit (22), generates a three-dimensional model of a living body corresponding to the value of the first parameter and the estimated value of the second parameter.

Description

Biological model generation device, biological model generation program, and biological model generation method

[0001] The present disclosure relates to a biological model generation device, a biological model generation program, and a biological model generation method.

[0002] Generally, products are evaluated through simulations using a human body model. For example, when evaluating a product that detects occupants in a vehicle cabin through image recognition, the accuracy of occupant detection may be evaluated through simulations using image data created using a three-dimensional human body model instead of two-dimensional image data of the occupants in the vehicle cabin.

[0003] A system for evaluating products through simulation using a human body model has been disclosed (see Patent Document 1). Patent Document 1 describes a system for evaluating a wide range of products by generating several types of human body models, such as a standard-sized human body model and a large-sized or small-sized human body model, by appropriately modifying human body shape data obtained by three-dimensionally measuring each part of the human body.

[0004] Japanese Patent Application Laid-Open No. 2002-259474

[0005] However, when appropriately modifying shape data of a living body, including the human body, to generate a new three-dimensional model with the desired outer shape, there is a problem in that, depending on the balance of the modifications to each part, the generated three-dimensional model may deviate from the natural outer shape of the living body.

[0006] The present disclosure aims to solve the above-mentioned problem and to provide a biological model generation device, a biological model generation program, and a biological model generation method that can generate a three-dimensional model of a living organism with a natural external shape.

[0007] The biological model generation device according to the present disclosure is characterized by comprising: a parameter acquisition unit that acquires a value of a first parameter related to the external shape of a specific living organism; an estimation unit that calculates an estimated value of a second parameter that indicates the external shape of the living organism corresponding to the value of the first parameter based on statistical data related to the external shapes of multiple living organisms of the same type as the specific living organism and the value of the first parameter acquired by the parameter acquisition unit; and a biological model generation unit that generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit.

[0008] According to the present disclosure, estimated values ​​of other parameters that indicate the external shape of a living organism are calculated based on statistical data regarding the external shape of the living organism and the values ​​of the parameters of the living organism, and a three-dimensional model of the living organism is generated based on the estimated values ​​of the calculated parameters, thereby making it possible to generate a three-dimensional model of the living organism with a natural external shape.

[0009] 1 is a block diagram showing a schematic configuration of a biological model generating device according to embodiment 1. 2 is a block diagram showing a configuration of a parameter estimation unit according to embodiment 1. 3 is a block diagram showing an example of the hardware configuration of a biological model generating device according to embodiment 1. 4 is a block diagram showing an example of the hardware configuration of a biological model generating device according to embodiment 1. 5 is a flowchart showing processing performed by the biological model generating device according to embodiment 1. 6 is a table showing an example of statistical data acquired by the biological model generating device according to embodiment 1. 7 is a block diagram showing a configuration of a parameter estimation unit according to embodiment 2. 8 is a block diagram showing a configuration of a parameter estimation unit according to embodiment 3. 9 is a block diagram showing a configuration of a parameter estimation unit according to embodiment 4. 10 is a block diagram showing a configuration of a parameter estimation unit according to embodiment 5.

[0010] Embodiments of the present disclosure will be described in detail below with reference to the drawings. Embodiment 1. First, the configuration of a biological model generation device 100 according to embodiment 1 will be described with reference to FIGS. 1 and 2. FIG. 1 is a block diagram showing a schematic configuration of the biological model generation device according to embodiment 1. The biological model generation device 100 according to embodiment 1 is a device that estimates values ​​of other parameters that indicate the external shape of a living organism based on the values ​​of specific parameters input by a user, and generates a three-dimensional model of the living organism based on these parameter values. The biological model generation device 100 also generates two-dimensional image data based on the generated three-dimensional model of the living organism. Note that the living organism for which the biological model generation device 100 generates a three-dimensional model may be a human body, a specific type of animal other than a human body, or any other living organism.

[0011] As shown in FIG. 1 , the biological model generation device 100 includes an input unit 10, a parameter estimation unit 20, a biological model generation unit 30, a virtual environment generation unit 40, and an output unit 50. The biological model generation device 100 is also electrically connected to an input device (not shown) and an external device (not shown) via wired or wireless connections so as to be able to communicate with each other. The input device inputs information to the biological model generation device 100. For example, the input device is a keyboard, mouse, or other device that accepts a user's input operation, and inputs information corresponding to the user's input operation to the biological model generation device 100. For example, the input device is another device, such as a computer, server, or database, connected to the biological model generation device 100, and inputs information to the biological model generation device 100 based on a predetermined trigger.

[0012] The external device receives information output from the biological model generating device 100. For example, the external device is configured with a display device such as a liquid crystal panel that displays the information output from the biological model generating device 100. Furthermore, for example, the external device is configured with a device such as a computer, server, or database that performs specific processing based on the information output from the biological model generating device 100. Note that the input device and the external device may be configured integrally with the biological model generating device 100, or may be configured with multiple devices electrically connected to each other, or the biological model generating device 100 may be configured with multiple devices electrically connected to each other.

[0013] The input unit 10 receives information from an input device. For example, the input unit 10 receives from the input device information indicating values ​​of one or more parameters related to the external shape of the living organism, which are used when generating a three-dimensional model of the living organism. In other words, the input unit 10 acquires information indicating values ​​of one or more parameters related to the external shape of the living organism, which are used when generating a three-dimensional model of the living organism. Note that in the first embodiment, the parameters related to the external shape of the living organism acquired by the input unit 10 from the input device are also referred to as first parameters. For example, the first parameters are parameters related to the overall external shape of the living organism or the external shapes of major parts of the living organism, and are major parameters for determining the external shape of the living organism.

[0014] For example, the input unit 10 receives, from the input device, information indicating the value of a parameter related to the body length of the living body and information indicating the value of a parameter related to the body shape of the living body, as information indicating the value of the first parameter. Specifically, the input unit 10 receives, from the input device, information indicating the height of the living body as a parameter related to the body length of the living body and information indicating the weight of the living body as a parameter related to the body shape of the living body. The body shape of a living body refers to classifications such as thin, obese, and normal, or the ratio of the length of a specific part of the living body to the length of other parts of the living body. Generally, in the same species of living body, there is a correlation between weight and body shape. Therefore, the weight of a living body can be considered a parameter related to the body shape of the living body. For example, in the case of a human, a parameter related to the body shape of a living body other than weight of the living body can be the circumference of a specific part of the living body, such as chest circumference or abdominal circumference. Generally, in the same species of living body, the greater the weight of the living body, the larger the external shape of the living body, and the smaller the weight of the living body, the smaller the external shape of the living body. Therefore, the weight of a living body can be considered a parameter related to the external shape of the living body.

[0015] Furthermore, for example, the input unit 10 receives, from the input device, information indicating the value of a parameter related to the size of the external shape of the living body along a first direction and information indicating the value of a parameter related to the size of the external shape of the living body along a second direction intersecting the first direction, as information indicating the value of the first parameter. Specifically, the input unit 10 receives, from the input device, information indicating the size of the living body in the longitudinal direction as the parameter related to the size of the external shape of the living body along the first direction, and information indicating the size of the living body in the width direction, which is a direction perpendicular to the longitudinal direction, as the parameter related to the size of the external shape of the living body along the second direction. Note that the information indicating the size of the living body in the width direction may be the length of the living body in the width direction or the circumference of the living body along the direction perpendicular to the longitudinal direction of the living body.

[0016] The first parameter is not limited to a parameter that can be directly expressed as a numerical value, such as the size of a specific body part. The first parameter may be a parameter that is usually expressed as a non-numerical value, such as gender or race. For example, by assigning a numerical value to each gender or race in advance, the gender or race as the first parameter can be treated as a numerical value. In addition to the above, other first parameters include age, generation, etc.

[0017] Furthermore, for example, the input unit 10 receives from the input device information for deforming the three-dimensional model of the living body and information regarding conditions for generating planar image data based on the three-dimensional model of the living body. For example, the input unit 10 receives from the input device information for changing the position and orientation of the three-dimensional model of the living body in a specific coordinate system as information for deforming the three-dimensional model of the living body. For example, the input unit 10 receives from the input device information for changing the posture, such as the angles of the joints of the three-dimensional model of the living body, as information for deforming the three-dimensional model of the living body. For example, the input unit 10 receives from the input device information for changing the facial expression of the three-dimensional model of the living body as information for deforming the three-dimensional model of the living body.

[0018] Furthermore, for example, the input unit 10 receives information from the input device for processing the three-dimensional model of a living organism, such as information for changing the appearance of the surface of the three-dimensional model, such as the color, pattern, fine irregularities, and light reflectance of the three-dimensional model of a living organism.

[0019] Furthermore, for example, the input unit 10 receives from the input device information regarding the conditions for generating planar image data based on a three-dimensional model of a living organism, such as information indicating the position of the viewpoint when generating planar image data of the three-dimensional model viewed from a specific viewpoint; information indicating the position, brightness, color of the light source, ambient scattered light, and other light irradiation conditions when generating planar image data in a state where light is irradiated from a specific light source onto the three-dimensional model; information regarding the background of the three-dimensional model when generating planar image data based on the three-dimensional model of a living organism; and information regarding other objects to be included in the planar image data together with the three-dimensional model when generating planar image data based on the three-dimensional model of a living organism.

[0020] The input device only needs to be configured to input information indicating the value of at least one parameter related to the external shape of the living organism into the input unit 10, and with regard to information other than information indicating the value of a parameter related to the external shape of the living organism, the input device may be configured to input all of the above-mentioned information into the input unit 10, or may be configured to input any one or more of the above-mentioned information into the input unit 10.

[0021] The parameter estimation unit 20 estimates the value of a parameter other than the first parameter that indicates the external shape of the living body. In the first embodiment, the parameter related to the external shape of the living body estimated by the parameter estimation unit 20 is also referred to as the second parameter. For example, the second parameter is a parameter related to the external shape of an individual part of the living body, and is a detailed parameter for determining the external shape of the living body by comparing it with the first parameter. FIG. 2 is a block diagram showing the configuration of the parameter estimation unit 20 according to the first embodiment. As shown in FIG. 2, the parameter estimation unit 20 according to the first embodiment includes a parameter acquisition unit 21, an estimation unit 22, and a storage unit 25.

[0022] The parameter acquisition unit 21 acquires the value of a first parameter of the living body via the input unit 10 in order to estimate the value of a second parameter of the living body. The storage unit 25 stores various information for estimating the value of the second parameter of the living body. The estimation unit 22 calculates an estimated value of the second parameter of the living body based on the value of one or more first parameters acquired by the parameter acquisition unit 21 and the information stored in the storage unit 25. For example, the estimation unit 22 calculates, as the value of the second parameter of the living body, estimated values ​​of dimensions of one or more parts of the living body, such as the size of the living body's head, shoulder width, chest circumference, arm length, arm thickness, leg length, and leg thickness. Furthermore, for example, the estimation unit 22 calculates, as the value of the second parameter of the living body, estimated values ​​of coordinates indicating the position of a specific part of the living body in a specific coordinate system.

[0023] For example, the estimation unit 22 calculates an estimated value of a second parameter of a living organism based on statistical data on the external shapes of a plurality of living organisms and the value of the first parameter acquired by the parameter acquisition unit 21. Specifically, the estimation unit 22 calculates an estimated value of the second parameter of a living organism based on statistical data of the second parameter for the first parameter of the living organism, which is calculated based on actual measured values ​​of the values ​​of the first parameter and the values ​​of the second parameter of a plurality of living organisms stored in advance in the storage unit 25, and the value of the first parameter acquired by the parameter acquisition unit 21.

[0024] Furthermore, for example, the estimation unit 22 calculates an estimated value of the second parameter of the living body based on a trained model generated based on statistical data of actual measurement values ​​of first parameter values ​​and second parameter values ​​of a plurality of living bodies, which is stored in advance in the storage unit 25, and the value of the first parameter acquired by the parameter acquisition unit 21. Specifically, the estimation unit 22 calculates an estimated value of the second parameter of the living body using a trained model that is stored in advance in the storage unit 25 and that is generated by machine learning such as deep learning using a data set of the value of the first parameter of the living body and statistical data of the value of the second parameter corresponding to the value of the first parameter, and that calculates the value of the second parameter corresponding to the first parameter by inputting the value of a specific first parameter. For example, the estimation unit 22 is a trained model stored in advance in the memory unit 25, and is generated by machine learning such as deep learning using statistical data obtained by surveying multiple human bodies as a data set, which includes a combination of data having height and weight values, which are first parameters of a specific human body, and shoulder width values, which are second parameters corresponding to the values ​​of the first parameters of the specific human body.By inputting specific height and weight values, the trained model calculates an estimated value of shoulder width using the trained model to calculate the shoulder width value corresponding to the height and weight.

[0025] Since the trained model is generated based on statistical data of parameters related to the external shapes of multiple living organisms, the estimation unit 22, which calculates an estimated value of the second parameter of the living organism using such a trained model and the value of the first parameter acquired by the parameter acquisition unit 21, can be said to calculate an estimated value of the second parameter of the living organism based on statistical data related to the external shapes of multiple living organisms and the value of the first parameter acquired by the parameter acquisition unit 21.

[0026] For example, such statistical data may include a representative value of the value of the second parameter corresponding to the value of the first parameter of the living body, a variability index, a probability density function, etc. By using such a trained model, the estimation unit 22 can calculate, as an estimated value, a representative value of the second parameter of the living body based on the value of the first parameter of the living body. Furthermore, by using such a trained model, the estimation unit 22 can calculate, as estimated values, a plurality of second parameters according to the distribution of variability of the second parameter of the living body corresponding to the value of the first parameter, based on the value of the first parameter of the living body.

[0027] The model used in machine learning may be a general linear regression model or a neural network. The model used in machine learning may also be Gaussian process regression. Using Gaussian process regression makes it possible to output statistical data of a second parameter based on the value of a specific first parameter. For example, using Gaussian process regression as a model used in machine learning makes it possible to calculate statistical data of shoulder width, such as the mean and variance of shoulder width, by inputting a combination of height and weight as a first parameter and outputting shoulder width corresponding to the input height and weight. Using such a model makes it possible to calculate an estimated value of the second parameter based on statistical data of the second parameter corresponding to the specific first parameter. Using such a model also makes it possible to calculate the substantial range of the second parameter corresponding to the specific first parameter.

[0028] It is preferable that the estimation unit 22 is configured to calculate an estimated value of a second parameter of the living body based on the values ​​of two or more first parameters acquired by the parameter acquisition unit 21. The estimation unit 22 outputs the calculation result of the estimated value to the biological model generation unit 30.

[0029] 1 generates a three-dimensional model of a living organism corresponding to the value of the first parameter and the estimated value of the second parameter, based on the value of the first parameter acquired by the input unit 10 and the estimated value of the second parameter calculated by the estimation unit 22. For example, the biological model generation unit 30 generates a three-dimensional model of a living organism, which is information indicating a three-dimensional shape expressed by polygon data, volume data, point cloud data, etc. Note that the biological model generation unit 30 may be configured to acquire the value of the first parameter via the parameter estimation unit 20.

[0030] Various known methods can be used to generate a three-dimensional model based on the value of the first parameter and the estimated value of the second parameter. For example, the biological model generation unit 30 can generate a three-dimensional model of a living organism based on the value of the first parameter and the estimated value of the second parameter by referencing information on a three-dimensional model of a standard living organism that serves as a base body and that is stored in advance in the storage unit 25, and adjusting the length and thickness of each part of the three-dimensional model based on the value of the first parameter and the estimated value of the second parameter. The biological model generation unit 30 outputs biological model information indicating the generated three-dimensional model of the living organism to the virtual environment generation unit 40.

[0031] The storage unit 25 may store a plurality of three-dimensional models corresponding to gender, race, and age as three-dimensional models of a standard living organism to be used as a base body, and the biological model generation unit 30 may be configured to select one of the plurality of three-dimensional models based on the value of the first parameter acquired by the input unit 10, and generate a three-dimensional model of the living organism based on the selected three-dimensional model according to the value of the first parameter and the estimated value of the second parameter. Furthermore, when the storage unit 25 stores three or more different three-dimensional models of a standard living organism to be used as a base body, the biological model generation unit 30 may be configured to select two or more three-dimensional models from the three or more three-dimensional models stored in the storage unit 25 based on either or both of the value of the first parameter and the estimated value of the second parameter, and to synthesize these three-dimensional models after weighting each of the two or more selected three-dimensional models according to either or both of the value of the first parameter and the estimated value of the second parameter.

[0032] The virtual environment generation unit 40, which functions as a three-dimensional model conversion unit, performs transformations, such as converting and adding information, on the three-dimensional model of the living organism generated by the biological model generation unit 30, and then generates planar image data including the three-dimensional model. For example, the virtual environment generation unit 40 transforms the three-dimensional model of the living organism generated by the biological model generation unit 30 into another three-dimensional model based on information for transforming the three-dimensional model acquired by the input unit 10. Specifically, the virtual environment generation unit 40 changes the posture of the three-dimensional model of the living organism generated by the biological model generation unit 30 based on information for changing the posture of the three-dimensional model. In other words, the virtual environment generation unit 40 transforms the three-dimensional model of the living organism generated by the biological model generation unit 30 so that the posture of the three-dimensional model of the living organism generated by the biological model generation unit 30 changes to a different posture based on information for changing the posture of the three-dimensional model. For example, if the living organism is a human body, posture may include whether the living organism is standing or sitting, the direction of the face relative to the orientation of the torso, whether the arms are crossed, etc.

[0033] Also, for example, the virtual environment generation unit 40 changes the position and orientation in a specific coordinate system of the three-dimensional model of the living body generated by the biological model generation unit 30 based on information for changing the position and orientation of the three-dimensional model in a specific coordinate system.

[0034] Furthermore, for example, the virtual environment generation unit 40 converts the three-dimensional model of the organism generated by the biological model generation unit 30 into planar image data viewed from a specific viewpoint. At this time, the virtual environment generation unit 40 converts the three-dimensional model of the organism generated by the biological model generation unit 30 into planar image data in a specific file format that virtually shows a state in which the organism represented by the three-dimensional model is located in a specific environment. For example, the virtual environment generation unit 40 converts the three-dimensional model of the organism generated by the biological model generation unit 30 into planar image data by including information about the background of the organism and objects present around the organism in the image based on parameter values ​​indicating the environment surrounding the organism acquired by the input unit 10, so that the converted planar image data becomes planar image data that virtually shows a state in which the organism is located in a specific environment, such as a room, a car cabin, a gymnasium, or an urban landscape.

[0035] Furthermore, for example, the virtual environment generation unit 40 converts the three-dimensional model of the living body generated by the living body model generation unit 30 into planar image data based on information acquired by the input unit 10 indicating the light irradiation conditions when the three-dimensional model is irradiated with light, so that the converted planar image data becomes planar image data that virtually indicates the state in which the living body is irradiated with light. Note that the specific environment in which the living body is located is not limited to one that imitates a realistic environment. The specific environment in which the living body is located may be one that matches the intended use of the planar image data, and may have a solid black background, for example. The virtual environment generation unit 40 outputs virtual environment information, which is information indicating the planar image data into which the three-dimensional model is converted, to the output unit.

[0036] The output unit 50 outputs the virtual environment information from the virtual environment generation unit 40 to an external device. For example, the external device virtually evaluates the operational performance of a device that performs image recognition using image data through a simulation, based on the virtual environment information acquired from the output unit 50. Also, for example, the external device generates learning data for a learning device that performs image recognition using image data, based on the virtual environment information acquired from the output unit 50.

[0037] Next, the hardware configuration of the biological model generating device 100 will be described with reference to FIGS. 3 and 4. FIG. 3 is a block diagram showing an example of the hardware configuration of the biological model generating device 100 according to embodiment 1, and FIG. 4 is a block diagram showing an example of a hardware configuration of the biological model generating device 100 according to embodiment 1 that is different from that shown in FIG. 2. For example, as shown in FIG. 3, the biological model generating device 100 includes a processor 100a, a memory 100b, and an I / O port 100c, and is configured so that the processor 100a reads and executes a program stored in the memory 100b. The memory 100b is configured, for example, by a non-volatile or volatile semiconductor memory such as a RAM, a ROM, a flash memory, an EPROM, or an EEPROM, or a combination thereof. The memory 100b may also be a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, a DVD, or the like. The memory 100b may also be an HDD or SSD.

[0038] 4, the biological model generating device 100 includes a processing circuit 100d and an I / O port 100c, which are dedicated hardware. The processing circuit 100d is configured, for example, by a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a system LSI (Large-Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each function of the biological model generating device 100 is realized by the processor 100a or the processing circuit 100d, which is dedicated hardware, executing a program that is software, firmware, or a combination of software and firmware. Note that the biological model generating device 100 may include hardware other than those described above.

[0039] Next, referring to Figures 1, 2, 5, and 6, the processing performed by the biological model generation device 100 according to embodiment 1 will be described using an example in which the living organism is a human body. Figure 5 is a flowchart showing the processing performed by the biological model generation device 100 according to embodiment 1. When starting the processing, the biological model generation device 100 first acquires, from the input device, values ​​of first parameters of a specific human body, i.e., a human body for which a three-dimensional model is to be generated (step ST1). For example, in this processing, the biological model generation device 100 acquires information indicating height as a value of the first first parameter and weight as a value of the second first parameter from the input device using the input unit 10.

[0040] After performing the process of step ST1, the biological model generating device 100 calculates an estimated value of the second parameter based on the value of the first parameter (step ST2). In this process, the biological model generating device 100 calculates an estimated value of the second parameter indicating the outer shape of the human body corresponding to the value of the first parameter based on statistical data on the outer shapes of multiple human bodies and the value of the first parameter for a specific human body acquired in the process of step ST1. For example, in this process, the biological model generating device 100 calculates estimated values ​​of shoulder width, arm length, and leg length as second parameters indicating the outer shape of the human body corresponding to the height and weight based on statistical data on the outer shapes of multiple human bodies acquired in advance by measuring the heights, weights, and other dimensions of each part of the human body and stored in the storage unit 25, and the height and weight of the specific human body acquired in the process of step ST1. For example, the statistical data acquired in advance and stored in the storage unit 25 includes a representative value of the value of the second parameter corresponding to the value of the first parameter and an index of variation in the value of the second parameter corresponding to the value of the first parameter.

[0041] FIG. 6 is a table showing an example of statistical data acquired by the biological model generating device 100 according to embodiment 1. Specifically, FIG. 6 is a table showing, as statistical data on shoulder width corresponding to height and weight as first parameters, the average value of shoulder width, which is a representative value, and the standard deviation, which is an index of shoulder width variation. For example, the table in FIG. 6 indicates that the average value of shoulder width for a human body whose height is 150 to 160 cm and whose weight is 50 to 60 kg is 35 cm, and the standard deviation of shoulder width for this human body is 5 cm. For example, in the process of step ST2, if the height and weight of a specific human body acquired in the process of step ST1 are 155 cm and 55 kg, respectively, the biological model generating device 100 calculates an estimated shoulder width of this human body to be 35 cm based on the table in FIG. 6.

[0042] By calculating the estimated value of the second parameter in this manner, even if the actual measurement value of the shoulder width of a human body of 155 cm and 55 kg has not been obtained, the estimation unit 22 can calculate the estimated value of the shoulder width based on statistical data obtained based on the actual measurement values ​​of a human body in the range of 150 to 160 cm and 50 to 60 kg.

[0043] Furthermore, depending on the application of the three-dimensional human body model, the biological model generating device 100 may be configured to calculate a value that is different from a representative value calculated based on statistical data on shoulder width as an estimated value of shoulder width corresponding to the height and weight of the specific human body obtained in the processing of step ST1. For example, depending on the application of the three-dimensional human body model, the biological model generating device 100 may be configured to calculate one or both of an effective upper limit and a lower limit of shoulder width taking variation into consideration as an estimated value of shoulder width corresponding to the height and weight of the specific human body obtained in the processing of step ST1.

[0044] Specifically, the biological model generation device 100 may be configured to calculate a lower limit of the shoulder width corresponding to the height and weight of the specific human body acquired in step ST1 by subtracting twice the standard deviation from the average value, and to calculate an upper limit of the shoulder width corresponding to the height and weight of the specific human body acquired in step ST1 by adding twice the standard deviation to the average value. For example, by calculating the substantial upper and lower limits of the second parameter in this manner, it is possible to determine the boundary value of the range in which the device can recognize images by simulating the evaluation of the operational performance of the device using virtual environment information generated based on the substantial upper and lower limits. Note that the above formula is an example of a formula for calculating the substantial upper or lower limit, and it is possible to set a formula for calculating the substantial upper or lower limit of a range that is considered statistically valid depending on the application of the three-dimensional human body model to be generated.

[0045] Furthermore, the statistical data used to calculate the estimated value of the second parameter is required to be statistical data about a living organism of the same species as the living organism for which the model is generated. For example, when calculating the estimated value of the second parameter of a human body, statistical data about animals other than humans cannot be used. Furthermore, by using statistical data about the same attributes of the living organism as the estimation target, such as age, gender, and race, more accurate estimation of the second parameter is possible.

[0046] Furthermore, the biological model generating device 100 may be configured to calculate multiple estimated values ​​as shoulder width estimates corresponding to the height and weight of the specific human body obtained in the processing of step ST1, depending on the application of the three-dimensional human body model to be generated. For example, the biological model generating device 100 may be configured to calculate multiple estimated values ​​such that the multiple shoulder width estimates corresponding to the height and weight of the specific human body obtained in the processing of step ST1 have a distribution of variation corresponding to statistical data, depending on the application of the three-dimensional human body model to be generated. By performing a simulation using multiple three-dimensional models generated based on the respective estimated values ​​of the multiple second parameters calculated in this manner or multiple planar image data generated based on each of these multiple three-dimensional models, it is possible to bring the results of the simulation closer to the results obtained when a simulation is performed using actual measurements of multiple living organisms.

[0047] After the processing of step ST2, the biological model generating device 100 generates biological model information representing a three-dimensional model of the human body based on the value of the first parameter acquired in the processing of step ST1 and the estimated value of the second parameter calculated in the processing of step ST2 (step ST3). For example, in this processing, the biological model generating device 100 generates biological model information representing a three-dimensional model of the human body having the height, shoulder width, arm length, and leg length based on the height and weight of the human body acquired in the processing of step ST1 and the estimated values ​​of shoulder width, arm length, and leg length calculated in the processing of step ST2.

[0048] After performing the processing of step ST3, the biological model generation device 100 generates virtual environment information, which is information obtained by converting the three-dimensional model, based on the biological model information generated in the processing of step ST3 (step ST4). For example, in this processing, the biological model generation device 100 generates virtual environment information, which is information indicating one or more pieces of planar image data obtained by converting one or more three-dimensional models, based on the biological model information generated in the processing of step ST3.

[0049] After performing the process of step ST4, the biological model generating device 100 outputs the generated virtual environment information to an external device (step ST5), and ends the process.

[0050] As described above, the biological model generating device 100 according to the first embodiment includes a parameter acquiring unit 21 that acquires the value of a first parameter related to the external shape of a specific living organism; an estimation unit 22 that calculates an estimate of a second parameter indicating the external shape of the living organism corresponding to the value of the first parameter based on statistical data related to the external shapes of multiple living organisms of the same type as the specific living organism and the value of the first parameter acquired by the parameter acquiring unit 21; and a biological model generating unit 30 that generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimate of the second parameter based on the value of the first parameter acquired by the parameter acquiring unit 21 and the estimate of the second parameter calculated by the estimation unit 22.

[0051] With this configuration, the biological model generation device 100 according to the first embodiment calculates an estimated value of a second parameter indicating the external shape of the living organism based on statistical data regarding the external shape of the living organism and the value of a first parameter of the living organism, and generates a three-dimensional model of the living organism based on the value of the first parameter and the calculated estimated value of the second parameter. This allows the generation of a three-dimensional model of the living organism with a more natural external shape than when a three-dimensional model is generated based solely on the value of the first parameter. An unnatural external shape refers to, for example, an external shape in which the dimensions themselves or the balance between multiple dimensions significantly deviate from a standard body shape determined based on statistical data. Furthermore, the biological model generation device 100 according to the first embodiment can generate a large number of three-dimensional models of living organisms with natural external shapes based on a large number of combinations of a small number of parameters. For example, the biological model generation device 100 according to the first embodiment can generate 100 three-dimensional models of living organisms with natural external shapes based on 100 combinations of different heights and weights. This allows the efficient use of a large number of three-dimensional models of living organisms with different external shapes to perform simulations such as image recognition simulations, physics simulations, and radio wave simulations.

[0052] In the first embodiment, the height and weight of a human body are described as examples of the first parameters, but the present invention is not limited to this. The first parameters may be parameters related to the external shape of a living body. For example, when generating, as virtual environment information, information showing planar image data of a human body in a seated state, it is preferable to use the sitting height of the human body as the first parameter instead of the height of the human body. Furthermore, in addition to the physical condition and weight of the living body, or instead of the weight of the living body, the first parameter may be a parameter indicating whether the living body falls into a body type classification such as thin, obese, or normal.

[0053] Furthermore, in the first embodiment, the biological model generating device 100 is configured to acquire statistical data used when calculating an estimated value of the second parameter from the storage unit 25, but is not limited to this. The biological model generating device may be configured to calculate, by the estimation unit, an estimated value of the second parameter indicating the external shape of a living organism corresponding to the value of the first parameter, based on statistical data related to the external shapes of a plurality of living organisms and the value of the first parameter acquired by the parameter acquisition unit, and may be configured, for example, to acquire the statistical data from an input device via the input unit when calculating an estimated value of the second parameter.

[0054] Furthermore, in the first embodiment, the biological model generation device 100 is configured to output planar image data as virtual environment information from the output unit 50 to an external device, but this is not limiting. The biological model generation device may be configured to output information generated based on a three-dimensional model as virtual environment information from the output unit to an external device. For example, the biological model generation device may be configured to output three-dimensional information as virtual environment information. For example, virtual environment information output as three-dimensional information can be used for three-dimensional simulations such as physical simulations and radio wave simulations. Furthermore, even when the biological model generation device outputs planar image data as virtual environment information, it may be configured to output the information as information different from information generally obtained by human vision, such as a distance image.

[0055] Second Embodiment Next, a biological model generating device according to a second embodiment will be described with reference to Fig. 7. The biological model generating device according to the second embodiment differs from the biological model generating device 100 according to the first embodiment in the configuration of the parameter estimation unit, but the other configurations are the same. Therefore, the same components as those in the first embodiment are denoted by the same reference numerals and names, and the description thereof will be omitted.

[0056] Fig. 7 is a block diagram showing the configuration of a parameter estimation unit 120 according to embodiment 2. As shown in Fig. 7, the parameter estimation unit 120 according to embodiment 2 includes a parameter acquisition unit 21, a first estimation unit 22, a second estimation unit 23, a third estimation unit 24, and a storage unit 25. Note that the first estimation unit 22 has the same function as the estimation unit according to embodiment 1, but differs in name, and will therefore be described using the same reference numeral as the estimation unit according to embodiment 1.

[0057] The first estimating unit 22, the second estimating unit 23, and the third estimating unit 24 calculate different estimated values ​​of the second parameter based on statistical data on the external shapes of a plurality of living organisms and the value of a specific first parameter acquired by the parameter acquiring unit 21. For example, based on statistical data on the height and weight of a human body and the height and weight of a specific human body, the first estimating unit 22 calculates an estimated value of the shoulder width corresponding to the height and weight, the second estimating unit 23 calculates an estimated value of the arm length corresponding to the height and weight, and the third estimating unit 24 calculates an estimated value of the leg length corresponding to the height and weight. Note that the number of estimating units included in the parameter estimating unit is not limited to two or three, and the parameter estimating unit may be configured to calculate different estimated values ​​of the second parameter using four or more estimating units.

[0058] Embodiment 3 Next, a biological model generating device according to embodiment 2 will be described with reference to Fig. 8. The biological model generating device according to embodiment 2 differs from the biological model generating device 100 according to embodiment 1 in the configuration of the parameter estimation unit, but other configurations are the same, and the same components as those in embodiment 1 are denoted by the same reference numerals and names, and description thereof will be omitted.

[0059] Fig. 8 is a block diagram showing the configuration of a parameter estimation unit 220 according to embodiment 3. As shown in Fig. 8, the parameter estimation unit 220 according to embodiment 3 includes a parameter acquisition unit 21, a first estimation unit 22, a second estimation unit 223, and a storage unit 25. Note that the first estimation unit 22 has the same function as the estimation unit according to embodiment 1, but differs in name, and will therefore be described using the same reference numeral as the estimation unit according to embodiment 1.

[0060] The first estimating unit 22 calculates an estimated value of a first second parameter based on statistical data related to the external shapes of a plurality of living organisms and the value of a specific first parameter acquired by the parameter acquiring unit 21. The second estimating unit 223 calculates an estimated value of a second second parameter correlated with the value of the first second parameter based on the statistical data and a calculation result of the estimated value by the first estimating unit 22. For example, the first estimating unit 22 calculates an estimated value of arm length as the first second parameter based on statistical data related to the external shapes of a plurality of living organisms and the height and weight of the human body. The second estimating unit 223 calculates an estimated value of forearm length as the second second parameter based on statistical data indicating the relationship between arm length and forearm length and a calculation result of the arm length estimate by the first estimating unit 22.

[0061] Configured in this manner, the biological model generating device according to embodiment 2 is capable of calculating the estimated values ​​of the second parameters so that the estimated values ​​are more natural compared to when the estimated values ​​of multiple second parameters are calculated individually.

[0062] Embodiment 4 Next, a biological model generation device according to embodiment 2 will be described with reference to Fig. 9. The biological model generation device according to embodiment 2 differs from the biological model generation device 100 according to embodiment 1 in the configuration of the parameter estimation unit, but other configurations are the same, and the same components as those in embodiment 1 are assigned the same reference numerals and names, and description thereof will be omitted.

[0063] Fig. 9 is a block diagram showing the configuration of a parameter estimation unit 320 according to embodiment 4. As shown in Fig. 9, the parameter estimation unit 320 according to embodiment 4 includes a parameter acquisition unit 21, a first estimation unit 22, a second estimation unit 323, and a storage unit 25. Note that the first estimation unit 22 has the same function as the estimation unit according to embodiment 1, but differs in name, and will therefore be described using the same reference numeral as the estimation unit according to embodiment 1.

[0064] The first estimator 22 calculates an estimate of a first second parameter based on statistical data on the external shapes of a plurality of living organisms and the value of a specific first parameter acquired by the parameter acquirer 21. The second estimator 323 calculates an estimate of a second second parameter correlated with the value of the first second parameter based on the statistical data, the value of the specific first parameter, and the calculation result of the estimate by the first estimator 22. For example, the first estimator 22 calculates an estimate of arm length as the first second parameter based on the statistical data on the external shapes of a plurality of living organisms and the height and weight of the human body. The second estimator 223 calculates an estimate of leg length as the second second parameter based on statistical data indicating the relationship between arm length and leg length, the height and weight of the human body, and the calculation result of the leg length estimate by the first estimator 22.

[0065] Configured in this manner, the biological model generating device according to the second embodiment is capable of calculating an estimate of the second parameter so that the estimate is more natural than when the estimates of multiple second parameters are calculated individually, even when the correlation between the first second parameter and the second second parameter is not strong enough to calculate an estimate of the second second parameter from only the estimate of the first second parameter.

[0066] Embodiment 5. Next, a biological model generating device according to embodiment 2 will be described with reference to Fig. 10. The biological model generating device according to embodiment 2 differs from the biological model generating device 100 according to embodiment 1 in the configuration of the parameter estimation unit, but other configurations are the same, and the same components as those in embodiment 1 are assigned the same reference numerals and names, and description thereof will be omitted.

[0067] Fig. 10 is a block diagram showing the configuration of a parameter estimation unit 420 according to embodiment 5. As shown in Fig. 10, the parameter estimation unit 420 according to embodiment 5 includes a parameter acquisition unit 21, an estimation unit 22, a determination unit 26, and a storage unit 25.

[0068] The determination unit 26 determines whether or not one or both of the value of the first parameter acquired by the parameter acquisition unit 21 and the estimated value of the second parameter calculated by the estimation unit 22 are appropriate values ​​for generating a three-dimensional model of a living organism. For example, if the value of the first parameter acquired by the parameter acquisition unit 21 is a value outside a predetermined range, the determination unit 26 determines that the value of the first parameter is inappropriate. For example, if the living organism of the three-dimensional model generated by the biological model generation device 100 is a human body, the determination unit 26 determines that the value of the first parameter is inappropriate based on a value of 10 cm for height as the first parameter.

[0069] Furthermore, for example, if the relationship between the value of a first first parameter and the value of a second first parameter acquired by the parameter acquisition unit 21 is not a preset relationship, the determination unit 26 determines that the value of the first parameter is inappropriate. Specifically, when the value of a first first parameter and the value of a second first parameter are acquired by the parameter acquisition unit 21, the determination unit 26 determines that the value of the first parameter is inappropriate if the value of the second first parameter is outside the range of the second first parameter set based on the value of the first first parameter. For example, if the living organism of the three-dimensional model generated by the biological model generation device 100 is a human body, the determination unit 26 determines that the values ​​of these first parameters are inappropriate based on the relationship between a height of 155 cm as the first first parameter and a weight of 15 kg as the second first parameter.

[0070] In this way, the range of values ​​of the first parameter used to determine whether the value of the first parameter acquired by the parameter acquisition unit 21 is an appropriate value is set in advance based on, for example, a range that is considered natural for the external shape of a living organism. The range of values ​​of the first parameter may also be set based on other conditions. For example, if a sufficient number of samples of statistical data for a second parameter corresponding to a specific first parameter value are not collected, and an estimated value of the second parameter is calculated based on the statistical data and the value of the first parameter, the calculated estimated value of the second parameter may be an unnatural value representing the external shape of a living organism. For this reason, the range of values ​​of the first parameter used to determine whether the value of the first parameter acquired by the parameter acquisition unit 21 is an appropriate value may be set based on a range that is considered to have a sufficient number of samples of statistical data for calculating an estimated value of the second parameter.

[0071] Furthermore, for example, if the estimated value of the second parameter calculated by the estimation unit 22 is a value outside a preset range, the determination unit 26 determines that the value of the second parameter is inappropriate. Furthermore, for example, if the relationship between the value of the first parameter acquired by the parameter acquisition unit 21 and the estimated value of the second parameter calculated by the estimation unit 22 is not a preset relationship, the determination unit 26 determines that the estimated value of the second parameter is inappropriate. Specifically, if the estimated value of the second parameter calculated by the estimation unit 22 is a value outside the range of the second parameter set based on the value of the first parameter acquired by the parameter acquisition unit 21, the determination unit 26 determines that the value of the second parameter is inappropriate. For example, when the living organism of the three-dimensional model generated by the biological model generation device 100 is a human body, the judgment unit 26 judges that the estimated value of the second parameter is inappropriate based on the relationship that the estimated value of sitting height as the second parameter calculated by the estimation unit 22 is 100 cm and the height as the first parameter acquired by the parameter acquisition unit 21 is 120 cm.

[0072] The biological model generation device according to the fifth embodiment generates a three-dimensional model of a living organism based on the determination result of the determination unit 26. For example, when the determination unit 26 determines that the value of the first parameter does not exceed a predetermined range, the biological model generation device according to the fifth embodiment generates a three-dimensional model of a living organism corresponding to the value of the first parameter and the estimated value of the second parameter, and when the determination unit 26 determines that the value of the first parameter exceeds the range, the biological model generation device does not generate a three-dimensional model of a living organism corresponding to the value of the first parameter and the estimated value of the second parameter.

[0073] Furthermore, for example, in the biological model generating device of embodiment 5, when the judgment unit 26 judges that the value of the second first parameter does not exceed a range that is preset based on the value of the first first parameter, the biological model generating device generates a three-dimensional model of the living organism corresponding to the value of the first first parameter, the value of the second first parameter, and the estimated value of the second parameter, and when the judgment unit 26 judges that the value of the second first parameter exceeds the above-mentioned range, the biological model generating device does not generate a three-dimensional model of the living organism corresponding to the value of the first first parameter, the value of the second first parameter, and the estimated value of the second parameter.

[0074] Furthermore, for example, in the biological model generating device of embodiment 5, if the judgment unit 26 determines that the value of the first parameter does not exceed a predetermined range, the output unit 50 outputs the three-dimensional model generated by the biological model generating unit 30 to an external device, and if the judgment unit 26 determines that the value of the first parameter exceeds the above-mentioned range, the output unit 50 does not output the three-dimensional model generated by the biological model generating unit 30.

[0075] Furthermore, for example, in the biological model generating device of embodiment 5, if the judgment unit 26 judges that the value of the second first parameter does not exceed a range that is preset based on the value of the first first parameter, the output unit 50 outputs the three-dimensional model generated by the biological model generating unit 30 to an external device, and if the judgment unit 26 judges that the value of the second first parameter exceeds the above-mentioned range, the output unit 50 does not output the three-dimensional model generated by the biological model generating unit 30.

[0076] Furthermore, when the biological model generation device according to the fifth embodiment determines that either or both of the value of the first parameter acquired by the parameter acquisition unit 21 and the estimated value of the second parameter calculated by the estimation unit 22 are inappropriate values ​​for generating a three-dimensional model of a living organism, it outputs a signal indicating the determination result. For example, when the biological model generation device determines that either or both of the value of the first parameter acquired by the parameter acquisition unit 21 and the estimated value of the second parameter calculated by the estimation unit 22 are inappropriate values ​​for generating a three-dimensional model of a living organism, it outputs a signal indicating the determination result to an alarm unit (not shown), and notifies the user of the determination result by the alarm unit. For example, the alarm unit may be configured with a speaker that emits sound based on an input signal, a display device that displays an image based on the input signal, a light-emitting device that changes the light emission mode based on the input signal, or the like. Furthermore, for example, when the biological model generation device determines that either or both of the value of the first parameter acquired by the parameter acquisition unit 21 and the estimated value of the second parameter calculated by the estimation unit 22 are not appropriate values ​​for generating a three-dimensional model of a living organism, the biological model generation device outputs a signal indicating the determination result to an external device and notifies the user of the external device of the determination result.

[0077] In the fifth embodiment, the biological model generation device is configured such that the determination unit 26 determines whether or not one or both of the value of the first parameter acquired by the parameter acquisition unit 21 and the estimated value of the second parameter calculated by the estimation unit 22 are appropriate values ​​for generating a three-dimensional model of the organism. However, this is not limiting. The biological model generation device may be configured to determine the appropriateness of a value acquired in any of the steps so that the three-dimensional model of the organism to be generated is appropriate. For example, the biological model generation device may be configured such that the estimation unit acquires an index of variation of the second parameter of the organism corresponding to the value of the first parameter based on statistical data of the second parameter, and the biological model generation unit generates a three-dimensional model of the organism if the index of variation is a value indicating smaller variation than a preset value, and does not generate a three-dimensional model of the organism if the index of variation is a value indicating larger variation than the preset value.

[0078] Furthermore, for example, the biological model generation device may be configured such that, when an index of variation of a second parameter of a living organism corresponding to the value of a first parameter, which is obtained based on statistical data of the second parameter, is a value indicating that the variation is smaller than a preset value, the output unit outputs a three-dimensional model of the living organism, and, when the index of variation is a value indicating that the variation is larger than a preset value, the output unit does not output a three-dimensional model of the living organism.

[0079] In addition, the present disclosure allows for free combination of the respective embodiments, modification of any of the components of the respective embodiments, or omission of any of the components of the respective embodiments.

[0080] The biological model generation device, biological model generation program, and biological model generation method according to the present disclosure can be used, for example, to generate a three-dimensional model of a living organism based on the values ​​of specific parameters.

[0081] Various aspects of the present disclosure are summarized below as appendices.

[0082] (Supplementary Note 1) A biological model generation device comprising: a parameter acquisition unit that acquires a value of a first parameter related to an external shape of a specific living organism; an estimation unit that calculates an estimated value of a second parameter indicating the external shape of the living organism corresponding to the value of the first parameter, based on statistical data on the external shapes of a plurality of living organisms of the same type as the specific living organism and the value of the first parameter acquired by the parameter acquisition unit; and a biological model generation unit that generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter, based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit. (Supplementary Note 2) The biological model generation device according to Supplementary Note 1, wherein the estimation unit calculates an estimated value of a second parameter of each of two or more living organisms corresponding to the value of the first parameter, based on the value of the first parameter, and the biological model generation unit generates three-dimensional models of the two or more living organisms corresponding to the value of the first parameter and the estimated value of the second parameter of each of the two or more living organisms. (Supplementary Note 3) The biological model generating device according to Supplementary Note 1 or 2, wherein the estimation unit calculates, as an estimated value, a value distant from a representative value of the second parameter set based on the statistical data. (Supplementary Note 4) The biological model generating device according to any one of Supplementary Notes 1 to 3, wherein the estimation unit calculates, as an estimated value, the second parameter in accordance with a distribution of variation indicated by the statistical data. (Supplementary Note 5) The biological model generating device according to any one of Supplementary Notes 1 to 4, wherein the parameter acquisition unit acquires a value of a first first parameter and a value of a second first parameter related to an external shape of the specific living organism, and the estimation unit calculates, based on the statistical data and the value of the first first parameter and the value of the second first parameter, an estimated value of the second parameter indicating the external shape of the living organism corresponding to the value of the first first parameter and the value of the second first parameter.(Supplementary Note 6) The biological model generation device according to any one of Supplements 1 to 5, wherein the first first parameter is a parameter relating to the body length of the specific living organism, and the second first parameter is a parameter relating to the body type of the specific living organism. (Supplementary Note 7) The biological model generation device according to any one of Supplements 1 to 6, wherein the first first parameter is a parameter indicating the height of the specific living organism, and the second first parameter is a parameter indicating the weight of the specific living organism. (Supplementary Note 8) The biological model generation device according to any one of Supplements 1 to 7, further comprising a determination unit that determines whether or not a value of the second first parameter exceeds a preset range based on the value of the first first parameter. (Supplementary Note 9) The biological model generation device according to any one of Supplementary Notes 1 to 8, wherein the biological model generation unit generates a three-dimensional model of the organism corresponding to the value of the first first parameter, the value of the second first parameter, and the estimated value of the second parameter, when the determination unit determines that the value of the second first parameter does not exceed the range, and does not generate a three-dimensional model of the organism corresponding to the value of the first first parameter, the value of the second first parameter, and the estimated value of the second parameter, when the determination unit determines that the value of the second first parameter exceeds the range. (Supplementary Note 10) The biological model generation device according to any one of Supplementary Notes 1 to 9, further comprising an output unit that outputs the three-dimensional model generated by the biological model generation unit, when the determination unit determines that the value of the second first parameter does not exceed the range, and does not output the three-dimensional model generated by the biological model generation unit, when the determination unit determines that the value of the second first parameter exceeds the range.(Supplementary Note 11) The biological model generation device described in any one of Supplementary Notes 1 to 10, characterized in that the estimation unit obtains an index of variation of a second parameter of the living organism corresponding to the value of the first parameter based on the statistical data, and the biological model generation unit generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter when the index of variation is a value indicating smaller variation than a preset value, and does not generate a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter when the index of variation is a value indicating larger variation than a preset value. (Supplementary Note 12) The biological model generation device according to any one of Supplementary Notes 1 to 11, further comprising an output unit that outputs the three-dimensional model generated by the biological model generation unit, wherein the output unit outputs the three-dimensional model of the organism generated by the biological model generation unit when an index of variation of a second parameter of the organism corresponding to the value of the first parameter, obtained based on the statistical data, is a value indicating smaller variation than a preset value, and does not output the three-dimensional model of the organism generated by the biological model generation unit when the index of variation is a value indicating larger variation than the preset value. (Supplementary Note 13) The biological model generation device according to any one of Supplementary Notes 1 to 12, further comprising an estimation unit that calculates, based on the statistical data and the value of the first parameter, an estimated value of a first second parameter indicating an external shape of the organism corresponding to the value of the first parameter and an estimated value of a second second parameter indicating the external shape of the organism corresponding to the value of the first parameter, (Supplementary Note 14) The biological model generating device described in any one of Supplementary Notes 1 to 13 is characterized in that the estimation unit calculates an estimated value of a first second parameter indicating the external shape of the living body corresponding to the value of the first parameter based on the statistical data and the value of the first parameter, and calculates an estimated value of a second second parameter indicating the external shape of the living body corresponding to the value of the first parameter based on the estimated value of the first second parameter.(Supplementary Note 15) The biological model generation device according to any one of Supplements 1 to 14, further comprising a steric model conversion unit that converts the steric model generated by the biological model generation unit so that the posture of the steric model becomes another posture. (Supplementary Note 16) The biological model generation device according to any one of Supplements 1 to 15, characterized in that the steric model conversion unit converts the steric model generated by the biological model generation unit into planar image data viewed from a specific viewpoint. (Supplementary Note 17) The biological model generation device according to any one of Supplements 1 to 16, characterized in that the steric model conversion unit generates planar image data including images of the steric model generated by the biological model generation unit and objects existing around the steric model. (Supplementary Note 18) The biological model generation device according to any one of Supplements 1 to 17, characterized in that the estimation unit calculates an estimated value of a second parameter that indicates the external shape of the organism corresponding to the value of the first parameter based on the value of the first parameter using a trained model. (Supplementary Note 19) A biological model generation program that causes a computer to function as: a parameter acquisition unit that acquires a value of a first parameter related to the external shape of a specific living organism; an estimation unit that calculates an estimated value of a second parameter that indicates the external shape of the living organism corresponding to the value of the first parameter, based on statistical data related to the external shapes of multiple living organisms of the same type as the specific living organism and the value of the first parameter acquired by the parameter acquisition unit; and a biological model generation unit that generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter, based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit.(Supplementary Note 20) A biological model generation method performed by an apparatus including a parameter acquisition unit, an estimation unit, and a biological model generation unit, comprising: a step in which the parameter acquisition unit acquires a value of a first parameter related to an external shape of a specific biological organism; a step in which the estimation unit calculates an estimated value of a second parameter indicating the external shape of the biological organism corresponding to the value of the first parameter based on statistical data related to the external shapes of multiple biological organisms of the same type as the specific biological organism and the value of the first parameter acquired by the parameter acquisition unit; and a step in which the biological model generation unit generates a three-dimensional model of the biological organism corresponding to the value of the first parameter and the estimated value of the second parameter based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit.

[0083] 10 Input unit (parameter acquisition unit), 20 Parameter estimation unit, 21 Parameter acquisition unit, 22 Estimation unit (first estimation unit), 23 Second estimation unit, 24 Third estimation unit, 25 Memory unit, 26 Determination unit, 30 Biological model generation unit, 40 Virtual environment generation unit (conversion unit), 50 Output unit, 100 Biological model generation device, 120 Parameter estimation unit, 220 Parameter estimation unit, 223 Second estimation unit, 320 Parameter estimation unit, 323 Second estimation unit, 420 Parameter estimation unit.

Claims

1. A biological model generation device comprising: a parameter acquisition unit that acquires the value of a first parameter related to the external shape of a specific living organism; an estimation unit that calculates an estimate of a second parameter that indicates the external shape of the living organism corresponding to the value of the first parameter based on statistical data related to the external shapes of multiple living organisms of the same type as the specific living organism and the value of the first parameter acquired by the parameter acquisition unit; and a biological model generation unit that generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit.

2. The biological model generation device of claim 1, characterized in that the estimation unit calculates an estimated value of a second parameter of each of two or more living organisms corresponding to the value of the first parameter based on the value of the first parameter, and the biological model generation unit generates a three-dimensional model of each of two or more living organisms corresponding to the value of the first parameter and the estimated value of the second parameter of each of the two or more living organisms.

3. The biological model generating device according to claim 1, characterized in that the estimation unit calculates an estimated value that is different from a representative value of the second parameter, which is set based on the statistical data.

4. The biological model generating device according to claim 1, wherein the estimation unit calculates the second parameter as an estimated value in accordance with the distribution of variation indicated by the statistical data.

5. The biological model generating device according to claim 1, characterized in that the parameter acquisition unit acquires a value of a first first parameter and a value of a second first parameter related to the external shape of the specific living organism, and the estimation unit calculates an estimated value of the second parameter indicating the external shape of the living organism corresponding to the value of the first first parameter and the value of the second first parameter based on the statistical data and the value of the first first parameter and the value of the second first parameter.

6. A biological model generating device according to claim 5, characterized in that the first first parameter is a parameter relating to the body length of the specific organism, and the second first parameter is a parameter relating to the body shape of the specific organism.

7. A biological model generating device according to claim 5, characterized in that the first first parameter is a parameter indicating the height of the specific living body, and the second first parameter is a parameter indicating the weight of the specific living body.

8. A biological model generating device according to claim 5, further comprising a judgment unit that judges whether the value of the second first parameter exceeds a range that is set in advance based on the value of the first first parameter.

9. The biological model generation device of claim 8, characterized in that the biological model generation unit generates a three-dimensional model of the living organism corresponding to the value of the first first parameter, the value of the second first parameter, and the estimated value of the second parameter when the judgment unit judges that the value of the second first parameter does not exceed the range, and does not generate a three-dimensional model of the living organism corresponding to the value of the first first parameter, the value of the second first parameter, and the estimated value of the second parameter when the judgment unit judges that the value of the second first parameter exceeds the range.

10. The biological model generation device according to claim 8, further comprising an output unit that outputs the three-dimensional model generated by the biological model generation unit, wherein the output unit outputs the three-dimensional model generated by the biological model generation unit when the judgment unit judges that the value of the second first parameter does not exceed the range, and does not output the three-dimensional model generated by the biological model generation unit when the judgment unit judges that the value of the second first parameter exceeds the range.

11. The biological model generation device of claim 1, characterized in that the estimation unit obtains an index of variation of a second parameter of the living body corresponding to the value of the first parameter based on the statistical data, and the biological model generation unit generates a three-dimensional model of the living body corresponding to the value of the first parameter and the estimated value of the second parameter when the index of variation is a value indicating a smaller variation than a preset value, and does not generate a three-dimensional model of the living body corresponding to the value of the first parameter and the estimated value of the second parameter when the index of variation is a value indicating a larger variation than a preset value.

12. The biological model generation device according to claim 1, further comprising an output unit that outputs the three-dimensional model generated by the biological model generation unit, wherein the output unit outputs the three-dimensional model of the organism generated by the biological model generation unit when an index of variation of a second parameter of the organism corresponding to the value of the first parameter, obtained based on the statistical data, is a value indicating smaller variation than a preset value, and does not output the three-dimensional model of the organism generated by the biological model generation unit when the index of variation is a value indicating larger variation than a preset value.

13. The biological model generating device according to claim 1, characterized in that the estimation unit calculates, based on the statistical data and the value of the first parameter, an estimated value of a first second parameter indicating the external shape of the living body corresponding to the value of the first parameter, and an estimated value of a second second parameter indicating the external shape of the living body corresponding to the value of the first parameter.

14. The biological model generating device according to claim 1, characterized in that the estimation unit calculates an estimated value of a first second parameter indicating the external shape of the living body corresponding to the value of the first parameter based on the statistical data and the value of the first parameter, and calculates an estimated value of a second second parameter indicating the external shape of the living body corresponding to the value of the first parameter based on the estimated value of the first second parameter.

15. The biological model generating device according to claim 1, further comprising a 3D model conversion unit that converts the 3D model generated by the biological model generating unit so that the pose of the 3D model becomes another pose.

16. A biological model generating device according to claim 15, wherein said three-dimensional model conversion unit converts the three-dimensional model generated by said biological model generating unit into two-dimensional image data viewed from a specific viewpoint.

17. The biological model generation device according to claim 16, characterized in that the three-dimensional model conversion unit generates two-dimensional image data including images of the three-dimensional model generated by the biological model generation unit and objects existing around the three-dimensional model.

18. A biological model generation device according to any one of claims 1 to 17, characterized in that the estimation unit calculates an estimated value of a second parameter indicating the external shape of a living organism corresponding to the value of the first parameter using a trained model based on the value of the first parameter.

19. A biological model generation program that causes a computer to function as: a parameter acquisition unit that acquires the value of a first parameter related to the external shape of a specific living organism; an estimation unit that calculates an estimated value of a second parameter that indicates the external shape of the living organism corresponding to the value of the first parameter based on statistical data related to the external shapes of multiple living organisms of the same type as the specific living organism and the value of the first parameter acquired by the parameter acquisition unit; and a biological model generation unit that generates a three-dimensional model of the living organism corresponding to the value of the first parameter and the estimated value of the second parameter based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit.

20. A biological model generation method performed by an apparatus having a parameter acquisition unit, an estimation unit, and a biological model generation unit, comprising: a step in which the parameter acquisition unit acquires the value of a first parameter related to the external shape of a specific biological organism; a step in which the estimation unit calculates an estimated value of a second parameter indicating the external shape of the biological organism corresponding to the value of the first parameter based on statistical data related to the external shapes of multiple biological organisms of the same type as the specific biological organism and the value of the first parameter acquired by the parameter acquisition unit; and a step in which the biological model generation unit generates a three-dimensional model of the biological organism corresponding to the value of the first parameter and the estimated value of the second parameter based on the value of the first parameter acquired by the parameter acquisition unit and the estimated value of the second parameter calculated by the estimation unit.

Images