Information processing device, information processing method, and information processing program

Abstract

It estimates a 3D body model that takes into account specific body parts. [Solution] The information processing device according to the present invention comprises a reception unit, an estimation unit, and a provision unit. The reception unit receives the designation of a location corresponding to a predetermined body part on a screen schematically displaying the user's body. The estimation unit estimates the location in the user's 3D body model that corresponds to the location designated on the screen. The provision unit provides information about the 3D body model, designating the estimated location in the 3D body model as the location corresponding to the predetermined body part.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Conventionally, techniques for estimating a 3D body model of a person or the like are known. For example, a technique for estimating a 3D body model of a state when the body shape changes such as a pose using a skeletal muscle model is known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Problems to be Solved by the Invention

[0004] However, in the conventional technology, it has been impossible to estimate a 3D body model in consideration of a predetermined part.

[0005] The present application has been made in view of the above, and an object thereof is to estimate a 3D body model in consideration of a predetermined part.

Means for Solving the Problems

[0006] The information processing apparatus according to the present application includes a reception unit that receives a designation of a position corresponding to a predetermined part on a screen schematically showing the body of a user, an estimation unit that estimates a position corresponding to the designated position on the screen among the 3D body models of the user, and a provision unit that provides information regarding the 3D body model with the estimated position among the 3D body models being a position corresponding to the predetermined part.

Effects of the Invention

[0007] According to one embodiment, it is possible to estimate a 3D body model that takes into account predetermined body parts. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of the configuration of an information processing system according to an embodiment. [Figure 2] Figure 2 is an explanatory diagram illustrating the entire information processing according to the embodiment. [Figure 3] Figure 3 is an explanatory diagram illustrating the estimation process of a 3D body model in a changed pose state. [Figure 4] Figure 4 is an explanatory diagram (1) illustrating an example of a method for assigning virtual markers according to the embodiment. [Figure 5] Figure 5 is an explanatory diagram (2) illustrating an example of a method for assigning virtual markers according to the embodiment. [Figure 6] Figure 6 is an explanatory diagram illustrating the process of changing the pose. [Figure 7] Figure 7 is an explanatory diagram illustrating the modification process using the first method. [Figure 8] Figure 8 is an explanatory diagram illustrating the modification process using the second method. [Figure 9] Figure 9 shows an example of the configuration of a user terminal according to this embodiment. [Figure 10] Figure 10 shows an example of the configuration of an information processing device according to an embodiment. [Figure 11] Figure 11 shows an example of a user information storage unit according to an embodiment. [Figure 12] Figure 12 shows an example of a learning model storage unit according to an embodiment. [Figure 13] Figure 13 is a flowchart (1) showing an example of information processing according to the embodiment. [Figure 14] Figure 14 is a flowchart (2) showing an example of information processing according to the embodiment. [Figure 15] FIG. 15 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing apparatus.

Best Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments for implementing the information processing apparatus, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program according to the present application are not limited by this embodiment. In addition, in each of the following embodiments, the same parts are denoted by the same reference numerals, and duplicate explanations are omitted.

[0010] (Embodiment) [1. Configuration of Information Processing System] The information processing system 1 shown in FIG. 1 will be described. As shown in FIG. 1, the information processing system 1 includes a user terminal 10 and an information processing apparatus 100. The user terminal 10 and the information processing apparatus 100 are communicably connected by wire or wirelessly via a predetermined communication network (network N). FIG. 1 is a diagram showing a configuration example of the information processing system for the embodiment.

[0011] The user terminal 10 is an information processing apparatus used by a user who is an estimation target of a 3D body model. The user is a user who desires an accurate estimation of a 3D body model in consideration of the size when the body shape such as a pose is changed. By accurately estimating a 3D body model in consideration of the size when the body shape is changed, for example, it becomes possible to create clothing (such as made-to-order) that fits the size feeling of the state when the body shape is changed.

[0012] The user terminal 10 may be any apparatus as long as it can realize the processing in the embodiment. Further, the user terminal 10 may be an apparatus such as a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, or a PDA. FIG. 2 shows a case where the user terminal 10 is a smartphone.

[0013] The user terminal 10 is a smart device such as a smartphone or a tablet, and is a portable terminal device capable of communicating with any server device via a wireless communication network such as 4G - 5G (Generation) or LTE (Long Term Evolution). Further, the user terminal 10 has a screen such as a liquid crystal display, and has a screen with a touch panel function, and may receive various operations on display data such as content, such as a tap operation, a slide operation, and a scroll operation, by a finger or a stylus from the user. In FIG. 2, the user terminal 10 is used by the user U1.

[0014] The information processing device 100 is an information processing device aimed at estimating a 3D body model considering an appropriate size when the body shape is changed. In the following embodiments, the information processing device 100 provides information regarding an accurate 3D body model considering an appropriate size, for example, when the pose is changed. The information processing device 100 is realized by, for example, a server device or a cloud system that proposes clothing suitable for the 3D body model of the user.

[0015] Note that the pose before the change / the pose after the change are hereinafter appropriately referred to as the "specific pose" / the "changed pose". In the following embodiments, the state of the normal pose with both hands down is described as the "specific pose", and the state of spreading both hands horizontally is described as the "changed pose". Also, in the following embodiments, "estimation" is described as being interchangeable with "generation", "measurement" or "measurement", "update" or "correction", etc. as appropriate. Also, "change" is described as being interchangeable with "deformation", etc. as appropriate. [[ID=!2]]

[0016] 〔2. An Example of Information Processing〕 The information processing device 100 performs two main processes: the first is the estimation of a 3D body model, and the second is the pose change process (i.e., the estimation of the changed pose). By combining these two processes, the information processing device 100 provides accurate information about a 3D body model that takes appropriate size into account, for example, when a pose is changed from a specific pose to a changed pose.

[0017] In the embodiments described below, the process of integrating the two processes will be described first, followed by a detailed explanation of each process. In the embodiments described below, "pose" is not limited to concepts based on changes in joint angles or positional relationships, but may also include concepts based on muscle movements such as the degree of force applied or the degree of twisting. Furthermore, in the embodiments described below, the "predetermined body part" is not limited to joint parts such as elbows or knees, but may be any part of the body that has physical characteristics.

[0018] Figure 2 is an explanatory diagram illustrating the entire information processing according to the embodiment. Using Figure 2, we will explain the information processing that integrates two processes. First, we will explain the first process. First, a position corresponding to a predetermined part is specified on the screen (step S11).

[0019] The information processing device 100 schematically displays the user U1's body on the screen and accepts the user's specification of a location corresponding to a predetermined part on the screen (step S101). For example, the information processing device 100 uses a virtual marker to specify the location. The virtual marker includes, for example, text indicating a predetermined part. For example, a virtual marker is added by user U1's operation on the screen (for example, tapping or clicking). Alternatively, the virtual marker may be added to the outline of the body. For example, the virtual marker may be added to the outline of user U1's body by tracing it.

[0020] The information processing device 100 accepts the designation of a location where a virtual marker is attached (hereinafter referred to as "marker location") as a location corresponding to a predetermined body part. For example, this is effective when a predetermined body part is hidden from view by clothing or other means. To give a specific example, the information processing device 100 instructs user U1 to "tap your navel," and when user U1 taps on the screen saying "roughly around here," a virtual marker is attached to the tapped location. The information processing device 100 then accepts the designation of this marker location as the location of user U1's "navel."

[0021] The virtual markers assigned to the user captured in this manner follow the user's movements. That is, once assigned, the virtual markers will follow the user's position even if the user U1 changes their pose. For example, the information processing device 100 can make the virtual markers follow the user's movements using known tracking technologies, such as known skeletal detection technology or image processing. In this way, the information processing device 100 can assign virtual markers to users instead of markers that were previously attached directly to the user's body or clothing.

[0022] Furthermore, virtual markers not only correspond to virtual markers, but also indicate attributes of the user's physical location, such as "elbow," "wrist," and "navel." For example, the information processing device 100 can instruct user U1 to "tap your navel," receive a specification of the user's physical location to which the attribute "navel" should be assigned, and associate the attribute "navel" with the specified location, thereby identifying which location on the user's body in the image has the attribute "navel." As will become clear in the explanation below, the information processing device 100 can use the identified "navel" attribute to identify which location on the 3D model corresponding to the user has the attribute "navel."

[0023] The information processing device 100 estimates the corresponding position of the marker position in the 3D body model (step S102). For example, the information processing device 100 estimates which part of the 3D body model corresponds to the marker position. The information processing device 100 then designates the estimated position as a predetermined body part. For example, if the predetermined body part is the "navel," then the location of the "navel" in the 3D body model is identified.

[0024] This 3D body model may be a general-purpose 3D body model, or it may be a 3D body model set up specifically for user U1 (for example, a 3D body model that schematically represents user U1's body).

[0025] In this way, the information processing device 100 estimates (or updates or modifies) a 3D body model that takes into account a predetermined part (step S103). For example, if the predetermined part is the "navel", the information processing device 100 estimates the 3D body model based on the estimated position of the "navel". In this way, the information processing device 100 estimates a 3D body model from a 2D image on the screen.

[0026] For example, suppose the information processing device 100 has received a specification for the position of the "navel" as a virtual marker. In such a case, the information processing device 100 identifies other positions that can be detected by skeletal detection technology, etc. (hereinafter, these may be referred to as "estimated positions"). Here, the attributes of the estimated positions are attributes that are already known in the 3D body model, and correspond to, for example, joint parts in the skeleton. The information processing device 100 then estimates the positional relationship and distance relationship between the estimated positions identified in the captured image and the position of the "navel," and estimates the position of the "navel" in the 3D body model from the estimated positional relationship and distance relationship and the estimated positions in the 3D body model. The information processing device 100 may also simultaneously modify the 3D model itself (for example, shoulder width or length from shoulder to elbow) based on the positional relationship and distance relationship between the estimated positions.

[0027] Furthermore, if multiple virtual markers are set, the information processing device 100 may modify the 3D body model based on the positional and distance relationships between the virtual markers, as well as identify the positions in the 3D body model that correspond to the virtual markers and identify the attributes of those positions. For example, if the information processing device 100 receives the specification of virtual markers for "shoulder," "elbow," and "navel," it will use image analysis technology to identify the positional and distance relationships of these multiple virtual markers and modify the user's 3D body model so that the identified positional and distance relationships are met. The information processing device 100 will then estimate the positions in the 3D body model that correspond to the virtual markers and assign attributes to the estimated positions that correspond to the virtual markers.

[0028] Furthermore, the 3D body model is estimated by updating (modifying) it according to the user U1's body (physical characteristics, etc.). This makes it possible to use, for example, the 3D body model of user U1. That concludes the first process. Next, I will explain the second process.

[0029] The information processing device 100 acquires an image of user U1 (step S104). For example, the information processing device 100 acquires an image of user U1 in a specific pose.

[0030] The information processing device 100 receives a specification of a modified pose from the user U1. Upon receiving the specification of a modified pose, the information processing device 100 estimates a 2D body model of the user U1 in the state of the specified modified pose (step S105). Note that the modified poses may be predetermined for each product (for example, clothing to be worn).

[0031] In this case, for example, the information processing device 100, upon receiving an image, estimates the 2D body model of user U1 using a machine learning model (hereinafter referred to as the "trained model") that has been trained to output information about a 2D body model in the state of a specified modified pose.

[0032] The information processing device 100 estimates the 3D body model of user U1 in the changed pose state based on the first processing result, the 3D body model (step S106). Then, the information processing device 100 provides the estimation result (step S107).

[0033] In this case, for example, the information processing device 100 takes a 2D body model of user U1 as input and uses a trained model that has been trained to output information about user U1's 3D body model to estimate the 3D body model of user U1 in the changed pose state.

[0034] Thus, based on the 3D body model, which is the result of the first processing step, the 3D body model of user U1 in the same pose is estimated from the 2D body model of user U1 in the changed pose. This makes it possible, for example, to use the 3D body model of user U1 in the changed pose. This concludes the second processing step. Next, we will explain the details of each processing step.

[0035] (First step: Details of the 3D body model estimation process) Generally, physical markers are directly attached to body models to change their pose or to clarify measurement points. However, in environments with high light levels, for example, the physical markers may not be clearly visible, making it difficult to recognize their positions. Therefore, when attaching markers to multiple locations, it may be necessary to change the type of physical marker or prepare markers suited to the environment.

[0036] Therefore, in the embodiment described below, virtual markers are attached to characteristic parts of a specific pose displayed on the terminal. This is expected to improve the accuracy of estimating the corresponding 3D body model. Furthermore, by manipulating the virtual markers, it becomes possible to estimate the 3D body model in the state of a changed pose.

[0037] Figure 3 is an explanatory diagram illustrating the estimation process of a 3D body model in a changed pose. In Figure 3, the user terminal 10 screen contains user U1 in a specific pose. Virtual markers (markers M1 and M2) are also attached to both wrists of user U1.

[0038] Furthermore, arrows P1 and P2 on the screen indicate the direction of operation relative to markers M1 and M2. For example, if arrow P1 is moved towards the upper left, marker M1 will move towards the upper left in accordance with the operation. In Figure 3, the pose changes from a normal posture with hands down to a posture with hands outstretched. Also in Figure 3, the operation is performed by photographer F1 (not shown) who is taking a picture of user U1.

[0039] The information processing device 100 acquires information from the screen. For example, the information processing device 100 acquires information about user U1 contained within the screen.

[0040] The information processing device 100 accepts the request for a changed pose from photographer F1 by having F1 assign virtual markers (markers M1 and M2) on the screen and perform operations.

[0041] In Figure 3, the information processing device 100 accepts a request for a change in pose to one where both arms are spread horizontally, when the photographer F1 manipulates the wrists of user U1 on the screen to add virtual markers (markers M1 and M2), and simultaneously manipulates marker M1 in the upper left direction and marker M2 in the upper right direction. The pose may change in response to the manipulation of the virtual markers, or the virtual markers may follow the movement of user U1's body (i.e., in response to the manipulation of the body), or the virtual markers may follow the movement of user U1.

[0042] The information processing device 100 estimates a 3D body model in the changed pose state through the second process described below. For example, the information processing device 100 estimates a 3D body model that takes into account the wrist area to which virtual markers (markers M1 and M2) are attached. In this case, for example, the information processing device 100 estimates a 3D body model that takes the wrist area into account by estimating the corresponding position of the wrist area of ​​the 3D body model from the marker positions of markers M1 and M2.

[0043] Figure 3 illustrates the example of applying a virtual marker to the wrist area, but virtual markers can also be applied to areas that cannot be visually judged. For example, virtual markers can be applied to areas hidden by clothing, such as the navel. By applying a virtual marker to an area like the navel, which cannot be visually judged, and estimating a 3D body model that takes the navel area into account, it becomes possible to accurately estimate the waist area of ​​the 3D body model.

[0044] Furthermore, as a further variation, the assignment of virtual markers is not limited to cases based on visual judgment, but may also be performed on areas where estimation accuracy is low. For example, virtual markers may be assigned to areas where there is room in the clothing (areas that sag). For example, virtual markers may be assigned to areas such as the "crotch" or "armpits." Then, for example, by assigning virtual markers to such areas with low estimation accuracy, such as the "crotch" or "armpits," and estimating a 3D body model that takes these areas into account, it becomes possible to accurately estimate the "crotch" and "armpit" areas of the 3D body model.

[0045] Here, we will describe a method for assigning virtual markers according to the embodiment. Three assignment methods will be described below as examples, but these examples are just examples and are not particularly limiting.

[0046] The first type of assignment is when an instruction area for aligning a predetermined part is clearly indicated on the screen. Figure 4 is an explanatory diagram (1) illustrating an example of a method for assigning virtual markers according to the embodiment.

[0047] In Figure 4, a virtual marker is displayed in the center of the screen of the user terminal 10. Simultaneously with the display of the virtual marker, an audio guide plays saying, "Align your navel with the red mark."

[0048] Here, user U1 may move relative to the virtual marker displayed on the screen to align with the position corresponding to the predetermined body part, or photographer F1 may move user terminal 10 to align with the position corresponding to the predetermined body part.

[0049] In either case, a virtual marker is placed on the part of the body aligned with the virtual marker in the center of the screen. The information processing device 100 then accepts the location where the virtual marker is placed as the location corresponding to a predetermined body part. In Figure 4, since the voice guide instruction is "navel," the location is accepted as the location corresponding to the "navel."

[0050] The second method of assignment involves using clothing that allows for the estimation of a 3D body model when worn. Such clothing contains map information indicating location for the estimation of the 3D body model. Therefore, by assigning virtual markers to the clothing, it becomes possible to associate specific body parts with the marker locations.

[0051] Figure 5 is an explanatory diagram (2) illustrating an example of a method for assigning virtual markers according to the embodiment. In Figure 5, clothing Z1 is used as an example of clothing that can be used to estimate such a 3D body model.

[0052] In Figure 5, as soon as the garment Z1 is put on, an audio guide plays saying, "Align your navel with the red mark," similar to Figure 4. The virtual marker can be aligned by either the user U1 or the photographer F1.

[0053] When the virtual marker is aligned, the information processing device 100 stores the marker position in association with a predetermined body part. In Figure 5, since the voice guide instructs "navel," the marker position is stored in association with the location of the "navel."

[0054] The information processing device 100 stores the marker position in association with the location of the "navel," so that when user U1 wears the clothing Z1 again, it can estimate the location of the "navel" based on the map information of the clothing Z1, without requiring specification by assigning a virtual marker.

[0055] A second variation of the assignment method involves using attachments such as stickers that enable the estimation of a 3D body model. Here, "sticker" refers to something different from the conventional physical markers mentioned above. Such attachments may contain, for example, map information indicating location, and by using multiple attachments, it becomes possible to estimate a 3D body model. For example, by attaching such attachments to clothing, it becomes possible to estimate a 3D body model not only with special clothing that enables 3D body model estimation as described above, but also with ordinary clothing worn daily.

[0056] The third type of assignment is when the person assigning the virtual marker (for example, user U1, photographer F1, or the recipient of information about the 3D body model) possesses specialized knowledge of the specified body part.

[0057] If the designator possesses specialized knowledge of the specified body part, the designation made by that designator is assumed to be accurate. The information processing device 100 accepts the designation of a location corresponding to the specified body part when the designator possesses such specialized knowledge of the specified body part. In other words, the information processing device 100 accepts the designation of a location to which a virtual marker has been placed by a designator with specialized knowledge of the specified body part as a location corresponding to the specified body part. For example, a designator with specialized knowledge of bone locations may place a virtual marker on the location of a specific bone.

[0058] (Second process: Details of the pose change process) In 3D body model estimation, the poses that can be estimated are limited to specific poses such as normal posture, because they must be postures that allow for accurate estimation of the entire body. Furthermore, there was a need for further improvement in the estimation accuracy of specific body parts. In addition, when modified poses were required, for example, manual estimation was sometimes necessary.

[0059] One example of a modified pose is a pose while riding a motorcycle. When riding a motorcycle, the lengths of the front and back of the upper body differ from those of a specific pose. Therefore, if clothing (such as a jacket) is created to match a specific pose, the length of the clothing may not fit the body properly. In addition, it was sometimes difficult to accurately select materials with different stretch rates and material areas for each part of the clothing (such as a motorcycle suit). For this reason, when it was necessary to create clothing to match a modified pose, it was necessary to re-estimate the user's modified pose.

[0060] Therefore, in the embodiment described below, a 3D body model of the state of a changed pose is estimated from a specific pose. This makes it possible to estimate the size of a desired part in the changed pose without requiring data from multiple poses. Furthermore, the embodiment described below is not limited to creating clothing that fits the user, but can also be applied to training such as gyms, Pilates, and yoga. For example, by estimating the length of the body's outer dimensions and comparing it with the previous estimate, it becomes possible to judge the effectiveness of training.

[0061] Figure 6 is an explanatory diagram illustrating the process of changing the pose. In the second process, the information processing device 100 associates the specific pose with the changed pose and estimates the changed pose from the input specific pose.

[0062] The information processing device 100 estimates the 3D body model of user U1 in a changed pose from the body model (2D body model or 3D body model) of user U1 in a specific pose.

[0063] In this case, the 3D body model may be estimated using a single learning model, or it may be estimated using a combination of multiple learning models.

[0064] When estimation is performed using a single learning model, the information processing device 100 takes a body model of the subject in a specific pose as input (the input information may be an image) and uses a learning model that has been trained to output information about the body model of the subject in a changed pose to estimate the body model of user U1 in a changed pose.

[0065] Here, we will explain the case where estimation is performed by combining multiple learning models. When performing estimation by combining multiple learning models, two methods are envisioned: one in which a 2D body model of the changed pose is estimated from a 2D body model of the specific pose, and a 3D body model of the changed pose is estimated from the 2D body model of the changed pose (hereinafter referred to as "Method 1" as appropriate); and another in which a 3D body model of the specific pose is estimated from a 2D body model of the specific pose, and a 3D body model of the changed pose is estimated from the 3D body model of the specific pose (hereinafter referred to as "Method 2" as appropriate).

[0066] Figure 7 is an explanatory diagram illustrating the modification process according to the first method. In the first method, the information processing device 100 takes a 2D body model of the subject in a specific pose as input (the input information may be an image) and uses a trained model that has been trained to output information about the 2D body model of the subject in a modified pose to estimate the 2D body model of user U1 in a modified pose. In this case, the information processing device 100 may also use an image generation AI to estimate the 2D body model of user U1 in a modified pose. For example, even if the abdomen is not visible in the modified pose, the information processing device 100 may use the image generation AI to estimate a 2D body model in a state where the abdomen is visible. Furthermore, the information processing device 100 may estimate the 3D body model of user U1 in a specific pose in the background. This makes it possible to compare it with the 3D body model in the modified pose by calculating the amount of change from the 3D body model in the modified pose after estimating the 3D body model in the modified pose, as described later.

[0067] The information processing device 100 then takes a 2D body model of the subject as input and estimates the 3D body model of user U1 using a trained model that has been trained to output information about the subject's 3D body model in the same pose. The information processing device 100 takes a 2D body model of user U1 in the changed pose as input and estimates the 3D body model of user U1 in the changed pose. In this case, the information processing device 100 may estimate the 3D body model of user U1 using a predetermined rule base, or it may estimate the 3D body model of user U1 based on a general conventional method from the contours of the image. Furthermore, the information processing device 100 may estimate the 3D body model of user U1 by dividing it into parts covered by clothing and parts not covered by clothing. This enables the information processing device 100 to estimate the 3D body model of user U1 in the changed pose with high accuracy.

[0068] In this case, the information processing device 100 may estimate the 3D body model of user U1 in the state of a modified pose by updating a 3D body model of a general-purpose modified pose. For example, the information processing device 100 may estimate the 3D body model of user U1 in the state of a modified pose by using a learning model that has been trained to output information about the 3D body model of a subject in the state of a modified pose by updating a 3D body model of a general-purpose modified pose.

[0069] Figure 8 is an explanatory diagram illustrating the modification process using the second method. In the second method, the information processing device 100 takes a 2D body model of the subject as input (the input information may also be an image) and uses a learning model trained to output information about the 3D body model of the subject in the same pose to estimate the 3D body model of user U1. The information processing device 100 takes a 2D body model of user U1 in a specific pose as input and estimates the 3D body model of user U1 in that specific pose. In this case, the information processing device 100 may estimate the 3D body model of user U1 using a predetermined rule base, or it may estimate the 3D body model of user U1 based on a general conventional method from the contour of the image.

[0070] In this case, the information processing device 100 may estimate the 3D body model of user U1 in a specific pose by updating a general-purpose 3D body model of a specific pose. For example, the information processing device 100 may estimate the 3D body model of user U1 in a specific pose by using a learning model that has been trained to output information about the 3D body model of a target person in a specific pose by updating a general-purpose 3D body model of a specific pose.

[0071] Then, the information processing device 100 takes a 3D body model of the subject in a specific pose as input and uses a learning model that has been trained to output information about the subject's 3D body model in a changed pose to estimate the 3D body model of user U1 in a changed pose. In this case, the information processing device 100 may also estimate the 3D body model of user U1 in a changed pose using a learning model that has been further trained with information about user U1's skeletal muscle (for example, information such as the starting point of movement).

[0072] [3. User terminal configuration] Next, the configuration of the user terminal 10 according to the embodiment will be described using Figure 9. Figure 9 is a diagram showing an example of the configuration of the user terminal 10 according to the embodiment. As shown in Figure 9, the user terminal 10 has a communication unit 11, an input unit 12, an output unit 13, and a control unit 14. Note that if the photographer is operating the terminal while photographing the user who is the target of the 3D body model estimation, the user may be read as photographer, the user terminal as photographer terminal, etc., as appropriate below.

[0073] (Communications Section 11) The communication unit 11 is implemented, for example, by a NIC (Network Interface Card). The communication unit 11 is connected to a predetermined network N by wire or wireless connection and sends and receives information to and from the information processing device 100 via the predetermined network N.

[0074] (Input section 12) The input unit 12 accepts various operations from the user. For example, the input unit 12 may accept various operations from the user via a touch panel display. Alternatively, the input unit 12 may accept various operations from buttons on the user terminal 10, or from a keyboard or mouse connected to the user terminal 10.

[0075] (Output section 13) The output unit 13 is a display screen for a tablet terminal or the like, implemented by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display, and is a display device for displaying various types of information. For example, the output unit 13 displays information provided by the information processing device 100.

[0076] (Control Unit 14) The control unit 14 is, for example, a controller, and is implemented by a CPU (Central Processing Unit) or MPU (Micro Processing Unit) executing various programs stored in the internal memory of the user terminal 10 using RAM (Random Access Memory) as the working area. For example, these various programs include application programs installed on the user terminal 10. For example, these various programs include application programs that assign virtual markers according to user operations. The control unit 14 is also implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0077] As shown in Figure 9, the control unit 14 has a receiving unit 141 and a transmitting unit 142, and realizes or executes the information processing operations described below.

[0078] (Receiver 141) The receiving unit 141 receives various types of information from other information processing devices, such as the information processing device 100. For example, the receiving unit 141 receives information about the user's body model estimated by other information processing devices, such as the information processing device 100. Also, for example, the receiving unit 141 receives information about the body model, with predetermined locations determined by other information processing devices, such as the information processing device 100. For example, the receiving unit 141 receives information based on the positional relationship between predetermined locations and other locations.

[0079] (Transmitter 142) The transmitting unit 142 transmits various types of information to other information processing devices, such as the information processing device 100. For example, the transmitting unit 142 transmits user operation information. For example, the transmitting unit 142 transmits information on the assignment of virtual markers based on user operations. Also, for example, the transmitting unit 142 transmits information on the specification of change poses based on user operations.

[0080] [4. Configuration of Information Processing Device] Next, the configuration of the information processing device 100 according to the embodiment will be described using Figure 10. Figure 10 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. As shown in Figure 10, the information processing device 100 has a communication unit 110, a storage unit 120, and a control unit 130. The information processing device 100 may also have an input unit (for example, a keyboard or mouse) that receives various operations from the administrator of the information processing device 100, and a display unit (for example, a liquid crystal display) for displaying various information.

[0081] (Communications Department 110) The communication unit 110 is implemented, for example, by a NIC. The communication unit 110 is connected to the network N by wire or wireless connection and sends and receives information to and from the user terminal 10 via the network N.

[0082] (Storage unit 120) The memory unit 120 is implemented by, for example, semiconductor memory elements such as RAM and flash memory, or storage devices such as hard disks and optical discs. As shown in Figure 10, the memory unit 120 has a user information memory unit 121 and a learning model memory unit 122.

[0083] The user information storage unit 121 stores user information. For example, the user information storage unit 121 stores information about the user's body model. For example, the user information storage unit 121 stores information indicating the user's physical characteristics. For example, the user information storage unit 121 stores location information of the user's physical characteristic parts. For example, the user information storage unit 121 stores information indicating the correspondence with clothing map information that enables the estimation of a 3D body model. Here, Figure 10 shows an example of the user information storage unit 121 according to the embodiment. As shown in Figure 11, the user information storage unit 121 has items such as "User ID" and "User Information".

[0084] "User ID" indicates identification information used to identify a user. "User Information" indicates user information. In the example shown in Figure 11, conceptual information such as "User Information #1" and "User Information #2" is shown to be stored in "User Information," but in reality, coordinate information indicating the location of the user's physical characteristic parts within the user's body model is stored there.

[0085] The learning model storage unit 122 stores learning models. For example, the learning model storage unit 122 stores learning models that estimate various body models. Here, Figure 12 shows an example of the learning model storage unit 122 according to the embodiment. As shown in Figure 12, the learning model storage unit 122 has items such as "Learning Model ID" and "Learning Model".

[0086] The "Learning Model ID" indicates identification information for identifying the learning model. The "Learning Model" indicates the learning model. In the example shown in Figure 12, conceptual information such as "Learning Model #1" and "Learning Model #2" is stored in "Learning Model," but in reality, information about various parameters that make up the learning model is stored there.

[0087] (Control unit 130) The control unit 130 is a controller, and is implemented, for example, by a CPU or MPU executing various programs stored in the memory device inside the information processing device 100 using RAM as the working area. Alternatively, the control unit 130 can be implemented by an integrated circuit such as an ASIC or FPGA.

[0088] As shown in Figure 10, the control unit 130 includes an acquisition unit 131, a reception unit 132, a first estimation unit 133, a second estimation unit 134, and a provision unit 135, and realizes or executes the information processing operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 10, and other configurations are also acceptable as long as they perform the information processing described later.

[0089] (Acquisition part 131) The acquisition unit 131 acquires various information from external information processing devices. The acquisition unit 131 also acquires various information from other information processing devices such as the user terminal 10.

[0090] The acquisition unit 131 acquires various information from the storage unit 120. The acquisition unit 131 also stores the acquired information in the storage unit 120.

[0091] The acquisition unit 131 acquires, for example, user operation information. For example, the acquisition unit 131 acquires virtual marker assignment information based on user operation. For example, the acquisition unit 131 acquires location information specified on a screen schematically representing the user's body. For example, the acquisition unit 131 acquires location information specified as a position corresponding to a predetermined part on a screen schematically representing the user's body.

[0092] Furthermore, the acquisition unit 131 acquires, for example, information about the user's body model in a predetermined pose. Also, the acquisition unit 131 acquires, for example, an image of the user in a predetermined pose.

[0093] (Reception desk 132) The reception unit 132 accepts, for example, the designation of a location corresponding to a predetermined body part. For example, the reception unit 132 accepts the designation of a location corresponding to a predetermined body part on a screen schematically representing the user's body. For example, the reception unit 132 accepts the designation of a location corresponding to a predetermined body part on content schematically representing the user's body.

[0094] Furthermore, the reception unit 132 accepts, for example, the designation of a location where a virtual marker is attached to content schematically representing the user's body as a location corresponding to a predetermined body part. For example, the reception unit 132 accepts, for example, the designation of a location where a virtual marker is attached to a 3D body model schematically representing the user's body as a location corresponding to a predetermined body part. Furthermore, for example, the reception unit 132 accepts, for example, the designation of a location where a virtual marker is attached to a captured image schematically representing the user's body as a location corresponding to a predetermined body part.

[0095] Furthermore, the reception unit 132 accepts, for example, the designation of a location where a virtual marker is attached to a virtual marker displayed on a screen schematically representing the user's body, by having the user on the screen align their position with the virtual marker displayed on the screen schematically representing the user's body, as a location corresponding to a predetermined body part.

[0096] Furthermore, the reception unit 132 accepts a designation made by a designator who has specialized knowledge of the designated body part, for example, when the designator who designates the location corresponding to the designated body part has specialized knowledge of the designated body part. For example, the reception unit 132 accepts a designation made by a designator who has specialized knowledge of the designated body part, when the designator who is the recipient of the information provided by the provision unit 135 (described later) has specialized knowledge of the designated body part.

[0097] Furthermore, the reception unit 132 accepts, for example, the designation of a location that corresponds to a predetermined part of the body where the accuracy of position estimation of the 3D body model is reduced due to the wearing of clothing.

[0098] (1st estimation part 133) The first estimation unit 133 estimates a position designated as corresponding to a predetermined body part, for example. For example, the first estimation unit 133 estimates a position in the user's body model that corresponds to a position designated as corresponding to a predetermined body part on a screen schematically showing the user's body. For example, the first estimation unit 133 estimates a position in the user's body model that corresponds to a position in the user's body that corresponds to a position designated as corresponding to a predetermined body part on a screen schematically showing the user's body.

[0099] Furthermore, the first estimation unit 133 estimates the positions corresponding to predetermined body parts when the clothing is worn, based on the correspondence with map information of the clothing that is pre-set for clothing that can be used to estimate a 3D body model when worn. For example, the first estimation unit 133 estimates the positions corresponding to predetermined body parts when the clothing is worn, based on the relationship between map information of the clothing that is pre-set for clothing that can be used to estimate a 3D body model when worn, and positions designated as corresponding to predetermined body parts on a screen schematically representing the user's body.

[0100] Furthermore, the first estimation unit 133 estimates the positions corresponding to predetermined body parts when the clothing is worn, based on, for example, the correspondence between the clothing map information based on appendages attached to the clothing for the purpose of estimating a body model and the positions designated as corresponding to predetermined body parts on a screen schematically representing the user's body.

[0101] (Second estimation section 134) The second estimation unit 134 estimates, for example, a body model of the user in a second pose that is different from the first pose. For example, the second estimation unit 134 estimates a body model that includes at least information indicating the user's physical characteristics (for example, numerical information indicating coordinates, size, position, body length, etc.).

[0102] Furthermore, the second estimation unit 134 estimates the user's 3D body model in the second pose by using a learning model that has been trained to output information about the subject's 3D body model in the second pose when it receives information about the subject's 2D body model in the first pose.

[0103] Furthermore, the second estimation unit 134 estimates the user's 2D body model in the second pose using a learning model that has been trained to output information about the subject's 2D body model in the second pose when it receives information about the subject's 2D body model in the first pose. In this case, the second estimation unit 134 may, in the background, estimate the user's 3D body model in the first pose using a learning model that has been trained to output information about the subject's 3D body model in the first pose when it receives information about the subject's 2D body model in the first pose. After estimating the 3D body model in the second pose as described later, it may calculate the amount of change and perform a comparison.

[0104] Furthermore, the second estimation unit 134 estimates the 3D body model of the user in a second pose by using a learning model that has been trained to output information about the 3D body model of the subject in the same pose when a 2D body model of the subject is input.

[0105] Furthermore, the second estimation unit 134 estimates the 3D body model of the user in a first pose by using a learning model that has been trained to output information about the subject's 3D body model in the same pose when information about the subject's 2D body model is input.

[0106] Furthermore, the second estimation unit 134 estimates the user's 3D body model in the second pose by using a learning model that has been trained to output information about the subject's 3D body model in the second pose when the subject's 3D body model is input.

[0107] Furthermore, the second estimation unit 134 estimates the 3D body model of the user in the second pose from the 3D body model of the user in the first pose, for example, using a learning model that has been further trained with skeletal muscle information. For example, the second estimation unit 134 estimates using a learning model that has been further trained with skeletal muscle information, including information such as the starting point of movement, which has been identified based on the skeletal muscle model. The skeletal muscle model may be a general-purpose learning model or a learning model tailored to each user.

[0108] Furthermore, the second estimation unit 134 estimates the user's 3D body model using a learning model that has been trained to output information about the subject's 3D body model by updating a pre-set general-purpose 3D body model when information about the subject's 2D body model is input.

[0109] (Provider 135) The providing unit 135 provides, for example, information about a body model. For example, the providing unit 135 provides information about a body model, with the position estimated by the first estimation unit 133 being the position corresponding to a predetermined body part. Also, for example, the providing unit 135 provides information about a body model estimated by the second estimation unit 134. Furthermore, the providing unit 135 provides, for example, information based on the positional relationship between a predetermined body part and other body parts in order to clarify the position corresponding to a predetermined body part.

[0110] [5. Information Processing Flow] Next, the information processing procedure by the information processing system 1 according to the embodiment will be described using Figures 13 and 14. Figures 13 and 14 are flowcharts (1) and (2) showing the information processing procedure by the information processing system 1 according to the embodiment.

[0111] As shown in Figure 13, the information processing device 100 accepts the designation of a location corresponding to a predetermined body part on the screen using a virtual marker (step S201). The information processing device 100 estimates the location in the user's 3D body model that corresponds to the location designated on the screen (step S202). The information processing device 100 provides information about the 3D body model of the user, designating the estimated location as the location corresponding to the predetermined body part (step S203).

[0112] As shown in Figure 14, the information processing device 100 acquires information about the user's 2D body model in the state of the first pose (step S301). The information processing device 100 estimates the user's 2D or 3D body model in the state of the second pose using a learned model (step S302). The information processing device 100 provides information about the estimated body model (step S303).

[0113] [6. Effects] As described above, the information processing device 100 according to the embodiment includes a reception unit 132, a first estimation unit 133, and a provision unit 135. The reception unit 132 receives the designation of a location corresponding to a predetermined body part on a screen schematically showing the user's body. The first estimation unit 133 estimates a location in the user's 3D body model that corresponds to the location designated on the screen. The provision unit 135 provides information about the 3D body model, designating the estimated location in the 3D body model as a location corresponding to a predetermined body part.

[0114] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model that takes into account, for example, a predetermined body part.

[0115] Furthermore, the reception unit 132 accepts requests on content that schematically represents the user's body.

[0116] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by taking into account, for example, a predetermined part specified on the content.

[0117] Furthermore, the reception unit 132 accepts the designation of a location where a virtual marker is attached on content schematically representing the user's body as a location corresponding to a predetermined body part.

[0118] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by taking into account a predetermined part specified using a virtual marker, for example.

[0119] Furthermore, the reception unit 132 accepts the designation of a location where a virtual marker has been attached to a 3D body model schematically representing the user's body, as the location corresponding to a predetermined body part.

[0120] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by considering, for example, a predetermined part of the 3D body model specified using a virtual marker.

[0121] Furthermore, the reception unit 132 accepts the designation of a location where a virtual marker is attached to a photographic image schematically representing the user's body, as the location corresponding to a predetermined body part.

[0122] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by considering, for example, a predetermined part specified using a virtual marker on a captured image.

[0123] Furthermore, the reception unit 132 accepts the designation of a location where a virtual marker is attached, based on the user's alignment with the virtual marker displayed on the screen, as a location corresponding to a predetermined body part.

[0124] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by taking into account a predetermined part specified by the user through alignment.

[0125] Furthermore, the reception unit 132 accepts the designation of a location where a virtual marker is attached as a predetermined body part, after the photographer who is taking a picture of the user on the screen aligns the virtual marker with the virtual marker displayed on the screen.

[0126] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by taking into account a predetermined part specified by the photographer performing alignment, for example.

[0127] Furthermore, the reception unit 132 accepts designations made by designators who possess specialized knowledge of the specified body parts.

[0128] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by taking into account, for example, a predetermined body part specified by a designated person with specialized knowledge.

[0129] Furthermore, the reception unit 132 accepts designations from designated persons who are recipients of information provided by the provision unit 135.

[0130] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by taking into consideration, for example, a predetermined body part specified by the designated person who is the recipient of the information.

[0131] Furthermore, the first estimation unit 133 estimates the position when the clothing is worn, based on a pre-established relationship between the map information of the clothing, which is set in advance for clothing that can be used to estimate a 3D body model when worn, and a position specified on the screen.

[0132] As a result, the information processing device 100 according to the embodiment can estimate an accurate 3D body model that takes into account predetermined body parts, for example, based on map information of special clothing, without having to specify predetermined body parts each time.

[0133] Furthermore, the first estimation unit 133 estimates the position when the clothing is worn, based on a pre-established relationship between the map information of the clothing, which is based on the attachments given to the clothing for the purpose of estimating the 3D body model, and the position specified on the screen.

[0134] As a result, the information processing device 100 according to the embodiment can estimate an accurate 3D body model that takes into account predetermined body parts, for example, based on map information of clothing based on attached objects, without having to specify predetermined body parts each time.

[0135] Furthermore, the reception unit 132 accepts designations for specific body parts where the accuracy of position estimation of the 3D body model is reduced due to the wearing of clothing.

[0136] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model, for example, by taking into account predetermined body parts where the estimation accuracy is low.

[0137] Furthermore, the first estimation unit 133 estimates the position corresponding to a specified position within the 3D body model that corresponds to the user's body.

[0138] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model by, for example, estimating the position corresponding to a specified predetermined part of the 3D body model.

[0139] Furthermore, the providing unit 135 provides information based on the positional relationship between a predetermined part and other parts in order to clarify the location corresponding to the predetermined part.

[0140] As a result, the information processing device 100 according to this embodiment can, for example, allow the recipient of the information to accurately determine the location corresponding to a predetermined part.

[0141] Furthermore, as described above, the information processing device 100 according to the embodiment includes an acquisition unit 131, a second estimation unit 134, and a provision unit 135. The acquisition unit 131 acquires an image of the user in a first pose. The second estimation unit 134 estimates the user's body model in a second pose, which is different from the first pose, based on the image acquired by the acquisition unit 131. The provision unit 135 provides information regarding the body model estimated by the second estimation unit 134.

[0142] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model that takes into account the sense of size when the body shape is changed.

[0143] Furthermore, the second estimation unit 134 estimates a body model that includes at least information indicating the user's physical characteristics.

[0144] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model that includes, for example, information indicating the user's physical characteristics.

[0145] Furthermore, the second estimation unit 134 estimates a body model that includes at least numerical information indicating the user's physical characteristics.

[0146] As a result, the information processing device 100 according to the embodiment can estimate a more accurate 3D body model that includes, for example, numerical information indicating the user's physical characteristics.

[0147] Furthermore, the second estimation unit 134 estimates the body model using a trained model that has been trained to output information about the body model of the subject's second pose when an image of the subject is input.

[0148] As a result, the information processing device 100 according to the embodiment can estimate an accurate body model, for example, by using a predetermined learning model.

[0149] Furthermore, the second estimation unit 134, upon receiving an image of the subject, uses a trained model that has been trained to output information regarding the subject's second pose's 2D body model to estimate the user's 2D body model as a body model.

[0150] As a result, the information processing device 100 according to the embodiment can, for example, estimate an accurate 2D body model of the second pose state by using a predetermined learning model.

[0151] Furthermore, the second estimation unit 134 estimates the user's 3D body model using a learning model that has been trained to output information about the subject's 3D body model in the same pose as the 2D body model, when the subject's 2D body model is input.

[0152] As a result, the information processing device 100 according to the embodiment can estimate an accurate 3D body model by, for example, using a predetermined learning model.

[0153] Furthermore, the second estimation unit 134 estimates the user's 3D body model using a learning model that has been trained to output information about the 3D body model of the subject in the same pose as the image when an image of the subject is input.

[0154] As a result, the information processing device 100 according to the embodiment can estimate an accurate 3D body model by, for example, using a predetermined learning model.

[0155] Furthermore, the second estimation unit 134, upon inputting the subject's 3D body model, uses a learning model trained to output information about the subject's 3D body model in a second pose to estimate the user's 3D body model as a body model.

[0156] As a result, the information processing device 100 according to the embodiment can, for example, estimate an accurate 3D body model of the second pose state by using a predetermined learning model.

[0157] Furthermore, the second estimation unit 134 performs estimation using a learning model that has been trained on the user's skeletal muscle information.

[0158] As a result, the information processing device 100 according to the embodiment can, for example, estimate an accurate 3D body model that takes into account the user's skeletal muscle information.

[0159] Furthermore, the second estimation unit 134, upon receiving an image of the subject, estimates the user's 3D body model using a learning model that has been trained to output information about the subject's 3D body model by updating a pre-set general-purpose 3D body model based on the image.

[0160] As a result, the information processing device 100 according to the embodiment can estimate a 3D body model more quickly and accurately by, for example, updating a general-purpose 3D body model.

[0161] [7. Hardware Configuration] Furthermore, the information processing device 100 (or the user terminal 10 and the information processing device 100) according to the above-described embodiment is realized by a computer 1000 having a configuration such as that shown in Figure 15. Figure 15 is a hardware configuration diagram showing an example of a computer that realizes the functions of the user terminal 10 and the information processing device 100. The computer 1000 has a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

[0162] The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400, controlling various components. The ROM 1300 stores boot programs executed by the CPU 1100 when the computer 1000 starts up, as well as programs that depend on the computer 1000's hardware.

[0163] The HDD1400 stores programs executed by the CPU1100, as well as data used by such programs. The communication interface1500 acquires data from other devices via a predetermined communication network and sends it to the CPU1100, and transmits data generated by the CPU1100 to other devices via the predetermined communication network.

[0164] The CPU 1100 controls output devices such as displays and printers, and input devices such as keyboards and mice, via the input / output interface 1600. The CPU 1100 acquires data from input devices via the input / output interface 1600. The CPU 1100 also outputs the generated data to output devices via the input / output interface 1600.

[0165] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 can be, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), tape media, magnetic recording medium, or semiconductor memory.

[0166] For example, when the computer 1000 functions as a user terminal 10 and information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control units 14 and 130 by executing programs loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be obtained from other devices via a predetermined communication network.

[0167] [8. Other] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0168] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0169] Furthermore, the embodiments described above can be combined as appropriate, as long as the processing content is not contradictory.

[0170] Although some embodiments of the present invention have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, starting with the embodiments described in the disclosure section of the invention.

[0171] Furthermore, the terms "section, module, unit" mentioned above can be replaced with "means" or "circuit," etc. For example, the acquisition unit can be replaced with acquisition means or acquisition circuit. [Explanation of Symbols]

[0172] 1. Information Processing System 10. User terminals 11 Communications Department 12 Input section 13 Output section 14 Control Unit 100 Information Processing Devices 110 Communications Department 120 Storage section 121 User information storage unit 122 Learning Model Memory Unit 130 Control Unit 131 Acquisition Department 132 Reception Department 133 1st estimation part 134 Second estimation part 135 Provision Department 141 Receiving Unit 142 Transmitter N Network

Claims

1. A reception unit that accepts the user's designation of a location corresponding to a specific body part on a screen that schematically represents the user's body, An estimation unit that estimates the position corresponding to the specified position on the screen within the user's 3D body model, A providing unit that provides information about the 3D body model, with the estimated position in the 3D body model corresponding to the predetermined body part, An information processing device characterized by having the following features.

2. The aforementioned reception unit is The aforementioned designation is accepted on content that schematically represents the user's body. The information processing apparatus according to feature 1.

3. The aforementioned reception unit is On the content schematically representing the user's body, the location where a virtual marker is attached is designated as the location corresponding to the predetermined body part. The information processing apparatus according to feature 2.

4. The aforementioned reception unit is In a 3D body model schematically representing the user's body, the position to which a virtual marker is attached is designated as the position corresponding to the predetermined body part. The information processing apparatus according to claim 3.

5. The aforementioned reception unit is On the captured image schematically representing the user's body, the position where a virtual marker is attached is designated as the position corresponding to the predetermined body part. The information processing apparatus according to claim 3.

6. The aforementioned reception unit is The user aligns their position with the virtual marker displayed on the screen, and the position to which the virtual marker is assigned is designated as the position corresponding to the predetermined part. The information processing apparatus according to feature 2.

7. The aforementioned reception unit is The photographer, who is taking a picture of the user on the screen, aligns the virtual marker displayed on the screen with the position where the virtual marker is attached, and the designated position is accepted as the position corresponding to the predetermined body part. The information processing apparatus according to feature 2.

8. The aforementioned reception unit is The designation will be accepted if the designator making the designation possesses specialized knowledge of the specified part. The information processing apparatus according to feature 1.

9. The aforementioned reception unit is The designated person, who is the recipient of the information provided by the aforementioned provisioning unit, accepts the aforementioned designation. The information processing apparatus according to feature 8.

10. The estimation unit, Based on the pre-established relationship between the map information of the garment, which is set in advance for garments that allow for the estimation of a 3D body model when worn, and the designated position on the screen, the position is estimated when the garment is worn. The information processing apparatus according to feature 1.

11. The estimation unit, Based on the map information of the clothing, which is based on the attachments made to the clothing for the purpose of estimating a 3D body model, and the pre-established relationship between the map information of the clothing and the designated position on the screen, the position is estimated when the clothing is worn. The information processing apparatus according to feature 1.

12. The aforementioned reception unit is The designation is accepted for a predetermined body part that causes the accuracy of position estimation of the 3D body model to decrease due to the wearing of clothing. The information processing apparatus according to claim 10 or 11.

13. The estimation unit, The system estimates the position corresponding to the specified position within the 3D body model that corresponds to the user's body. The information processing apparatus according to feature 1.

14. The aforementioned supply unit is, To clarify the location corresponding to the predetermined part, information based on the positional relationship between the predetermined part and other parts is provided. The information processing apparatus according to feature 1.

15. A method of information processing performed by a computer, A reception process in which the user specifies the location corresponding to a designated body part on a screen that schematically represents the user's body, An estimation step of estimating the position corresponding to the specified position on the screen within the user's 3D body model, A provision step of providing information about the 3D body model, where the estimated position among the 3D body model corresponds to the predetermined body part, An information processing method characterized by including

16. A registration procedure that accepts the user's designation of a location corresponding to a specific body part on a screen that schematically represents the user's body, An estimation procedure for estimating the position corresponding to the specified position on the screen within the user's 3D body model, A procedure for providing information about the 3D body model, wherein the estimated position in the 3D body model corresponds to the predetermined body part, and the information about the 3D body model is provided. An information processing program characterized by causing a computer to execute it.

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