Animation character modeling defect detection system

Abstract

The invention relates to the technical field of three-dimensional modeling, in particular to a cartoon character modeling defect detection system which comprises a storage module used for editing character model motion codes and storing the character model motion codes; the capturing module is used for selecting the character model motion code stored in the storage module and driving the character model to move based on the character model motion code so as to capture a character model three-dimensional animation; the disassembling module is used for receiving the three-dimensional animation of the character model captured in the capturing module and disassembling the character model in the three-dimensional animation of the character model; the driving condition of the constructed character model is provided by editing the character model motion codes, so that the character model is subjected to defect detection in the motion state, and compared with an existing character modeling defect static detection technology, the detection precision is higher, the detection effect is more comprehensive, and the detection efficiency is improved. The character modeling defect which cannot be detected by static detection can be detected, and the character modeling compatibility is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of three-dimensional modeling, and in particular to a cartoon character modeling defect detection system. Background Art

[0002] Anime character modeling is to build a three-dimensional image based on the character settings through professional software and technology. Starting from the precise construction of the skeleton structure, the facial contour, body proportion and other appearance features of the character are carefully carved, and then rich materials and colors are given to make it have realistic texture and unique style. Whether it is a cute or cool character, it can be vividly presented in the virtual world.

[0003] The invention patent with application number 202311001222.0 discloses a real-scene 3D model defect detection method, which is characterized by comprising: obtaining a real-scene 3D model; the real-scene 3D model includes real-scene 3D model data and texture pictures of a half-edge data structure; obtaining camera extrinsics, and performing off-screen rendering on the real-scene 3D model based on the camera extrinsics to obtain corresponding two-dimensional images and depth maps; obtaining a deep learning data set, preprocessing the deep learning data set to obtain a training set, and training a preset deep learning model based on the training set to obtain a defect detection model; the deep learning model is a Unetformer network model, and the Unetformer network includes an Encoder part and a Decoder part. The encoder part is a Resnet-18 network, and the decoder part adopts an attention mechanism based on Transformer; the two-dimensional image and the depth map are input into the defect detection model to obtain the two-dimensional coordinates of the image plane corresponding to the defect point cloud, and the two-dimensional coordinates of the image plane corresponding to the defect point cloud are converted into three-dimensional coordinates in the real-scene three-dimensional model and the defect point cloud is colored to obtain a defect patch: the acquisition of camera extrinsic parameters includes: performing Poisson disk sampling on the real-scene three-dimensional model to obtain corresponding sampling points; determining the camera extrinsic parameters based on the position of the sampling point, the rendering distance and the normal unit vector of the sampling point: the camera extrinsic parameters include the position and rendering direction of the rendering camera.

[0004] This application aims to solve the following problems: "Due to the complex format and large amount of data of real-life 3D models, manual quality inspection has the following shortcomings: 1) The workload is huge, which puts a great burden on vision; 2) Different staff members have different standards for defect detection; 3) It is easy to miss detections. Currently, most of the research focuses on texture quality assessment and defect detection in the modeling and mapping process, and there are few studies on defect detection of models automatically generated by modeling software. Therefore, the current real-life 3D model defect detection methods have the problems of huge workload, low efficiency and easy missed detections."

[0005] However, in the process of constructing a character model, the current three-dimensional character modeling technology is difficult to take into account the actions that the character needs to complete later. Therefore, the current character modeling defect detection technology is difficult to achieve dynamic defect detection.

[0006] Therefore, a cartoon character modeling defect detection system is proposed. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a cartoon character modeling defect detection system, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An animation character modeling defect detection system, comprising:

[0010] The storage module is used to edit the motion code of the character model and store the motion code of the character model; the capture module is used to select the motion code of the character model stored in the storage module, drive the motion of the character model based on the motion code of the character model, so as to capture the three-dimensional animation of the character model; the disassembly module is used to receive the three-dimensional animation of the character model captured in the capture module, disassemble the character model in the three-dimensional animation of the character model, and separate the limbs of the character model in the three-dimensional animation of the character model at each joint position; the hiding module is used to select a limb model as a non-hidden target on the disassembled character model, and use the remaining unselected limb models as hidden targets to hide them in the three-dimensional animation of the character model; the acquisition module is used to obtain only There is a three-dimensional animation of a character model with a non-hidden target limb model, the three-dimensional animation of the character model is played, and the motion space of the non-hidden target limb model limited by the motion trajectory is collected based on the played three-dimensional animation of the character model; an analysis module is used to receive the motion space collected in the collection module, and use the three-dimensional space where the three-dimensional animation of the character model is located to place all the received motion spaces, and based on the placement results of the motion space in the three-dimensional space, analyze the modeling defect tendency of the character model; a judgment module is used to receive the modeling defect tendency analysis result of the character model in the analysis module, and use the analysis result to determine whether the character model has modeling defects; a feedback module is used to receive the judgment result in the judgment module and feedback to the system end user.

[0011] Furthermore, the character model motion code stored in the storage module is manually edited by a system end user, and the character model motion code includes: a plurality of groups of continuous time stamps, each group of time stamps is bound to a plurality of groups of position coordinates, and each group of position coordinates is provided with a distinguishing mark;

[0012] Each distinguishing mark corresponds to a limb joint on the character model, and the character model is in a three-dimensional space. The character model is placed based on a group of character model motion codes with the earliest timestamps in the character model motion codes. Further, based on the timestamp sequence of the character model motion codes, the position coordinates of the same mark are continuously moved and transformed to capture the three-dimensional animation of the character model.

[0013] After capturing the three-dimensional animation of the character model, the capture module stores the three-dimensional animation of the character model synchronously, and the distinguishing mark of the position coordinate setting is any one of numbers or text names of limb joints.

[0014] Furthermore, the storage module is provided with submodules at the lower level, including:

[0015] An uploading unit, used for uploading a character model;

[0016] A marking unit, used for marking each character model motion code, wherein the marking content is another group of character model motion codes in the storage module that has the highest similarity with the character model motion code;

[0017] Among them, the character models uploaded in the uploading unit are synchronously stored in the storage module, and during the operation phase of the marking unit, the similarities between the motion codes of each character model are synchronously identified, and the marking operation is further performed based on the similarity identification result.

[0018] Furthermore, the similarity recognition logic for the character model motion coding in the marking unit is:

[0019] Receive the character model motion codes stored in the storage module, select any two character model motion codes to perform similarity recognition, and make all combinations of character model motion codes in pairs perform similarity recognition operations:

[0020]

[0021] Where: SIMM (A, B) is the similarity between character model motion coding A and character model motion coding B; n is the set of vectors in character model motion coding; A i Encode the value of the i-th vector in A for the character model motion; B i Encode the value of the i-th vector in B for the character model motion;

[0022] Among them, the larger the value of SIMM(A,B), the higher the similarity of the motion coding of the two groups of character models.

[0023] Furthermore, the capture module operates in accordance with the following when selecting the character model motion encoding:

[0024] S1: setting the number of character model motion codes to be selected, and selecting an equal number of character model motion codes from the character model motion codes stored in the storage module;

[0025] S2: traverse the marked character model motion codes of each character model motion code, and identify whether the marked character model motion code of the character model motion code exists in the character model motion code selected last time;

[0026] S3: discarding the character model motion codes that are determined to exist, and selecting the discarded number of character model motion codes in the storage module again;

[0027] S4: jump to S2;

[0028] S5: Repeat S2-S4 until the number of selected character model motion codes is equal to the set number of character model motion code selections.

[0029] Furthermore, in the construction stage of the character model, each limb model of the character model is independently constructed, and then the independently constructed limb models are spliced ​​to obtain the character model;

[0030] The acquisition module runs synchronously with the hidden module, the hidden module runs continuously in the system, the number of consecutive runs of the hidden module in the system is equal to the number of limb models, and during the continuous running of the hidden module, the non-hidden targets selected in each run are different;

[0031] The motion space of the non-hidden target limb model in the acquisition module is limited by the motion trajectory in the three-dimensional animation of the character model. After acquiring the motion space of the non-hidden target limb model, the acquisition module synchronously stores the motion space of the non-hidden target limb model;

[0032] Among them, the motion space of the non-hidden target limb model is a set of three-dimensional space models.

[0033] Furthermore, the modeling defect tendency analysis logic of the character model in the analysis module is expressed as:

[0034]

[0035] Where: F is the modeling defect tendency value; j and j+1 are two sets of adjacent motion spaces;

[0036] Among them, when the intersection of two adjacent sets of motion spaces j and j+1 is an empty set, the modeling defect tendency value F is zero. In all other cases, the modeling defect tendency value F is 1. Based on the above formula, calculations are performed on all two adjacent motion spaces.

[0037] Furthermore, the determination module is provided with submodules at a lower level, including:

[0038] A sniffing unit, used to sniff defective limb models in the character model;

[0039] Among them, during the operation stage of the sniffing unit, all motion spaces with modeling defect tendency values ​​F of 1 are obtained, and the limb model to which the motion space that is the intersection source of the two motion spaces belongs is further determined as a defective limb model, that is, the defective limb model in the sniffed character model.

[0040] Furthermore, the feedback module is connected to a computer device that plays the three-dimensional animation of the character model via a wireless network, and the computer device is used as a feedback target;

[0041] Among them, the content fed back by the feedback module to the system end user also includes the limb model pointed to by the sniffing result of the sniffing unit.

[0042] Furthermore, the storage module is interactively connected to an upload unit and a marking unit at a lower level via a wireless network, the storage module is interactively connected to a capture module, a decomposition module, a hiding module, an analysis module and a determination module at a lower level via a wireless network, the determination module is interactively connected to a sniffing unit at a lower level via a wireless network, and the determination module is interactively connected to a feedback module via a wireless network.

[0043] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0044] The present invention provides a cartoon character modeling defect detection system. During operation, the system provides driving conditions for constructing a character model by editing the character model motion coding, so that the character model can be detected for defects in a moving state. Compared with the existing character modeling defect static detection technology, the system has higher detection accuracy and more comprehensive detection effect, and can detect character modeling defects that cannot be detected by static detection, thereby improving the compatibility of character modeling and providing greater optimization space for the movements and actions performed in the later stage of character modeling. At the same time, the system can also sniff the limb model of the character model with modeling defects, thereby assisting the modeling user to improve the character model more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1A schematic diagram of the structure of an animation character modeling defect detection system. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The present invention will be further described below in conjunction with the embodiments.

[0049] Embodiment 1:

[0050] A cartoon character modeling defect detection system of this embodiment, such as Figure 1 As shown, including:

[0051] A storage module, used for editing the character model motion code and storing the character model motion code;

[0052] The storage module is provided with submodules at the lower level, including:

[0053] An uploading unit, used for uploading a character model;

[0054] A marking unit, used for marking each character model motion code, wherein the marking content is another group of character model motion codes in the storage module that has the highest similarity with the character model motion code;

[0055] The character models uploaded in the uploading unit are synchronously stored in the storage module, and the marking unit is in operation stage, synchronously identifying the similarities between the motion codes of the character models, and further performing the marking operation based on the similarity recognition result;

[0056] The similarity recognition logic for the character model motion coding in the marking unit is:

[0057] Receive the character model motion codes stored in the storage module, select any two character model motion codes to perform similarity recognition, and make all combinations of character model motion codes in pairs perform similarity recognition operations:

[0058]

[0059] Where: SIMM (A, B) is the similarity between character model motion coding A and character model motion coding B; n is the set of vectors in character model motion coding; A i Encode the value of the i-th vector in A for the character model motion; B iEncode the value of the i-th vector in B for the character model motion;

[0060] Among them, the larger the value of SIMM(A,B), the higher the similarity of the motion coding of the two groups of character models;

[0061] By setting the above logic formula, a specified marking logic is provided for the marking of the character model motion coding.

[0062] A capture module, used for selecting the character model motion code stored in the storage module, and driving the character model to move based on the character model motion code to capture the three-dimensional animation of the character model;

[0063] The capture module runs when selecting the character model motion encoding, subject to:

[0064] S1: setting the number of character model motion codes to be selected, and selecting an equal number of character model motion codes from the character model motion codes stored in the storage module;

[0065] S2: traverse the marked character model motion codes of each character model motion code, and identify whether the marked character model motion code of the character model motion code exists in the character model motion code selected last time;

[0066] S3: discarding the character model motion codes that are determined to exist, and selecting the discarded number of character model motion codes in the storage module again;

[0067] S4: jump to S2;

[0068] S5: Repeat S2-S4 until the number of selected character model motion codes is equal to the set number of character model motion code selections;

[0069] A disassembly module is used to receive the character model three-dimensional animation captured by the capture module, disassemble the character model in the character model three-dimensional animation, and separate the limbs of the character model in the character model three-dimensional animation at various joint positions;

[0070] A hiding module is used to select a limb model on the disassembled character model as a non-hidden target, and the remaining unselected limb models are used as hidden targets to be hidden in the three-dimensional animation of the character model;

[0071] A collection module is used to obtain a three-dimensional animation of a character model with only a non-hidden target limb model, play the three-dimensional animation of the character model, and collect the motion space of the non-hidden target limb model limited by the motion trajectory based on the played three-dimensional animation of the character model;

[0072] An analysis module is used to receive the motion spaces collected by the collection module, place all received motion spaces using the three-dimensional space where the three-dimensional animation of the character model is located, and analyze the modeling defect tendency of the character model based on the placement result of the motion spaces in the three-dimensional space;

[0073] The modeling defect tendency analysis logic of the character model in the analysis module is expressed as:

[0074]

[0075] Where: F is the modeling defect tendency value; j and j+1 are two sets of adjacent motion spaces;

[0076] Among them, when the intersection of two adjacent sets of motion spaces j and j+1 is an empty set, the modeling defect tendency value F is zero, and in other cases, the modeling defect tendency value F is 1. Based on the above formula, all two adjacent motion spaces are calculated;

[0077] By setting the above logic formula, the system in this embodiment analyzes the modeling defect tendency of the character model, and provides necessary operation data support for the further operation of the judgment module in the system.

[0078] A determination module, used to receive the modeling defect tendency analysis result of the character model in the analysis module, and use the analysis result to determine whether the character model has modeling defects;

[0079] The judgment module is provided with submodules at the lower level, including:

[0080] A sniffing unit, used to sniff defective limb models in the character model;

[0081] In the operation phase of the sniffing unit, all motion spaces with a modeling defect tendency value F of 1 are obtained, and the limb model to which the intersection source motion space of the two motion spaces belongs is further determined as a defective limb model, that is, a defective limb model in the sniffed character model;

[0082] The feedback module is used to receive the judgment result from the judgment module and provide feedback to the system end user;

[0083] The storage module is interactively connected to an upload unit and a marking unit at its lower level through a wireless network, the storage module is interactively connected to a capture module, a decomposition module, a hiding module, an analysis module and a determination module through a wireless network, the determination module is interactively connected to a sniffing unit at its lower level through a wireless network, and the determination module is interactively connected to a feedback module through a wireless network.

[0084] In this embodiment, the storage module runs to edit the character model motion code and stores the character model motion code. The uploading unit synchronously uploads the character model. The marking unit marks each character model motion code in real time. The marking content is another set of character model motion codes in the storage module that have the highest similarity to the character model motion code. The capture module, in the post-operation, selects the character model motion code stored in the storage module, drives the character model to move based on the character model motion code to capture the three-dimensional animation of the character model. The disassembly module further receives the three-dimensional animation of the character model captured in the capture module, disassembles the character model in the three-dimensional animation of the character model, separates the limbs of the character model in the three-dimensional animation of the character model at each joint position, and then the hiding module selects a limb model as a non-hidden target on the disassembled character model, and performs the hidden module on the remaining unselected limb models. As a hidden target, it is hidden in the three-dimensional animation of the character model. The acquisition module runs to obtain the three-dimensional animation of the character model with only the non-hidden target limb model, and the three-dimensional animation of the character model is played. Based on the played three-dimensional animation of the character model, the motion space of the non-hidden target limb model limited by the motion trajectory is collected. The analysis module then receives the motion space collected in the acquisition module, and uses the three-dimensional space where the three-dimensional animation of the character model is located to place all the received motion spaces. Based on the placement result of the motion space in the three-dimensional space, the modeling defect tendency of the character model is analyzed. The judgment module runs to receive the modeling defect tendency analysis result of the character model in the analysis module, and uses the analysis result to determine whether the character model has modeling defects. The sniffing unit synchronously sniffs the defective limb model in the character model. Finally, the judgment result in the judgment module is received through the feedback module, and feedback is given to the system end user.

[0085] By running the system in the above embodiment, the character model is dynamically detected after modeling to detect the positions of defects on the surface of the character model, which more comprehensively ensures that the character model will not have defects such as penetration during movement during the later animation production process, thereby effectively improving the accuracy of character modeling.

[0086] Embodiment 2:

[0087] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The following is a further detailed description of a cartoon character modeling defect detection system in Example 1:

[0088] The character model motion code stored in the storage module is manually edited by the system end user, and the character model motion code includes: a plurality of groups of continuous time stamps, each group of time stamps is bound to a plurality of groups of position coordinates, and each group of position coordinates is provided with a distinguishing mark;

[0089] Each distinguishing mark corresponds to a limb joint on the character model, and the character model is in a three-dimensional space. The character model is placed based on a group of character model motion codes with the earliest timestamps in the character model motion codes. Further, based on the timestamp sequence of the character model motion codes, the position coordinates of the same mark are continuously moved and transformed to capture the three-dimensional animation of the character model.

[0090] After capturing the three-dimensional animation of the character model, the capture module stores the three-dimensional animation of the character model synchronously, and the distinguishing mark of the position coordinate setting is any one of numbers or text names of limb joints.

[0091] Through the above settings, the storage operations performed by the character model in the storage module are further limited, providing more indicative data support for the subsequent module operations of the system in Example 1, ensuring stable operation of the system.

[0092] like Figure 1 As shown, in the construction stage of the character model, each limb model of the character model is independently constructed, and then the independently constructed limb models are spliced ​​to obtain the character model;

[0093] The acquisition module runs synchronously with the hidden module. The hidden module runs continuously in the system. The number of consecutive runs of the hidden module in the system is equal to the number of limb models. During the continuous running of the hidden module, the non-hidden targets selected in each run are different.

[0094] The motion space of the non-hidden target limb model limited by the motion trajectory in the acquisition module is acquired in the three-dimensional animation of the character model. After acquiring the motion space of the non-hidden target limb model, the acquisition module synchronously stores the motion space of the non-hidden target limb model.

[0095] Among them, the motion space of the non-hidden target limb model is a set of three-dimensional space models.

[0096] Through the above settings, further operation logic limitation is provided for the operation of the system in Example 1, thereby ensuring stable operation of the system in Example 1.

[0097] like Figure 1 As shown, the feedback module is connected to the computer device that plays the three-dimensional animation of the character model through a wireless network, and the computer device is used as the feedback target;

[0098] Among them, the content fed back by the feedback module to the system end user also includes the limb model pointed to by the sniffing result of the sniffing unit.

[0099] Through the above settings, the interactive logic of the feedback module in the system in Example 1 is further limited, ensuring that the system operation results in Example 1 can be stably output and obtained by system end users, thereby appropriately optimizing and improving the character model.

[0100] In summary, during the operation of the system in the above embodiment, the driving conditions of the constructed character model are provided by editing the motion coding of the character model, so that the character model can be detected for defects in a moving state. Compared with the existing static detection technology for character modeling defects, the detection accuracy is higher and the detection effect is more comprehensive. It can detect character modeling defects that cannot be detected by static detection, thereby improving the compatibility of character modeling and providing greater optimization space for the movements and actions performed in the later stage of character modeling. At the same time, the system can also sniff the limb models on the character models with modeling defects, thereby assisting the modeling users to improve the character models more quickly.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cartoon character modeling defect detection system, characterized in that: include: A storage module, used for editing the character model motion code and storing the character model motion code; A capture module, used for selecting the character model motion code stored in the storage module, and driving the character model to move based on the character model motion code to capture the three-dimensional animation of the character model; A disassembly module is used to receive the character model three-dimensional animation captured by the capture module, disassemble the character model in the character model three-dimensional animation, and separate the limbs of the character model in the character model three-dimensional animation at various joint positions; A hiding module is used to select a limb model on the disassembled character model as a non-hidden target, and the remaining unselected limb models are used as hidden targets to be hidden in the three-dimensional animation of the character model; A collection module is used to obtain a three-dimensional animation of a character model with only a non-hidden target limb model, play the three-dimensional animation of the character model, and collect the motion space of the non-hidden target limb model limited by the motion trajectory based on the played three-dimensional animation of the character model; An analysis module is used to receive the motion spaces collected by the collection module, place all received motion spaces using the three-dimensional space where the three-dimensional animation of the character model is located, and analyze the modeling defect tendency of the character model based on the placement result of the motion spaces in the three-dimensional space; A determination module, used to receive the modeling defect tendency analysis result of the character model in the analysis module, and use the analysis result to determine whether the character model has modeling defects; The feedback module is used to receive the judgment result from the judgment module and provide feedback to the system end user.

2. The animation character modeling defect detection system according to claim 1, characterized in that: The character model motion code stored in the storage module is manually edited by a system end user, and the character model motion code includes: a plurality of groups of continuous time stamps, each group of time stamps is bound to a plurality of groups of position coordinates, and each group of position coordinates is provided with a distinguishing mark; Each distinguishing mark corresponds to a limb joint on the character model, and the character model is in a three-dimensional space. The character model is placed based on a group of character model motion codes with the earliest timestamps in the character model motion codes. Further, based on the timestamp sequence of the character model motion codes, the position coordinates of the same mark are continuously moved and transformed to capture the three-dimensional animation of the character model. After capturing the three-dimensional animation of the character model, the capture module stores the three-dimensional animation of the character model synchronously, and the distinguishing mark of the position coordinate setting is any one of numbers or text names of limb joints.

3. The animation character modeling defect detection system according to claim 1, characterized in that: The storage module is provided with submodules at the lower level, including: An uploading unit, used for uploading a character model; A marking unit, used for marking each character model motion code, wherein the marking content is another group of character model motion codes in the storage module that has the highest similarity with the character model motion code; Among them, the character models uploaded in the uploading unit are synchronously stored in the storage module, and during the operation phase of the marking unit, the similarities between the motion codes of each character model are synchronously identified, and the marking operation is further performed based on the similarity identification result.

4. The animation character modeling defect detection system according to claim 3, characterized in that: The similarity recognition logic for the character model motion coding in the marking unit is: Receive the character model motion codes stored in the storage module, select any two character model motion codes to perform similarity recognition, and make all combinations of character model motion codes in pairs perform similarity recognition operations: Where: SIMM (A, B) is the similarity between character model motion coding A and character model motion coding B; n is the set of vectors in character model motion coding; A i Encode the value of the i-th vector in A for the character model motion; B i Encode the value of the i-th vector in B for the character model motion; Among them, the larger the value of SIMM(A,B), the higher the similarity of the motion coding of the two groups of character models.

5. The animation character modeling defect detection system according to claim 1, characterized in that: The capture module operates in accordance with the following when selecting the character model motion encoding: S1: setting the number of character model motion codes to be selected, and selecting an equal number of character model motion codes from the character model motion codes stored in the storage module; S2: traverse the marked character model motion codes of each character model motion code, and identify whether the marked character model motion code of the character model motion code exists in the character model motion code selected last time; S3: discarding the character model motion codes that are determined to exist, and selecting the discarded number of character model motion codes in the storage module again; S4: jump to S2; S5: Repeat S2-S4 until the number of selected character model motion codes is equal to the set number of character model motion code selections.

6. The animation character modeling defect detection system according to claim 1, characterized in that: In the character model construction stage, each limb model of the character model is independently constructed, and then the independently constructed limb models are spliced ​​to obtain the character model; The acquisition module runs synchronously with the hidden module, the hidden module runs continuously in the system, the number of consecutive runs of the hidden module in the system is equal to the number of limb models, and during the continuous running of the hidden module, the non-hidden targets selected in each run are different; The motion space of the non-hidden target limb model in the acquisition module is limited by the motion trajectory in the three-dimensional animation of the character model. After acquiring the motion space of the non-hidden target limb model, the acquisition module synchronously stores the motion space of the non-hidden target limb model; Among them, the motion space of the non-hidden target limb model is a set of three-dimensional space models.

7. The animation character modeling defect detection system according to claim 1, characterized in that: The modeling defect tendency analysis logic of the character model in the analysis module is expressed as follows: Where: F is the modeling defect tendency value; j and j+1 are two sets of adjacent motion spaces; Among them, when the intersection of two adjacent sets of motion spaces j and j+1 is an empty set, the modeling defect tendency value F is zero. In all other cases, the modeling defect tendency value F is 1. Based on the above formula, calculations are performed on all two adjacent motion spaces.

8. The animation character modeling defect detection system according to claim 1, characterized in that: The determination module is provided with submodules at the lower level, including: A sniffing unit, used to sniff defective limb models in the character model; Among them, during the operation stage of the sniffing unit, all motion spaces with modeling defect tendency values ​​F of 1 are obtained, and the limb model to which the motion space that is the intersection source of the two motion spaces belongs is further determined as a defective limb model, that is, the defective limb model in the sniffed character model.

9. The animation character modeling defect detection system according to claim 1, characterized in that: The feedback module is connected to the computer device that plays the three-dimensional animation of the character model through a wireless network, and the computer device is used as the feedback target; Among them, the content fed back by the feedback module to the system end user also includes the limb model pointed to by the sniffing result of the sniffing unit.

10. The animation character modeling defect detection system according to claim 1, characterized in that: The storage module is interactively connected to an upload unit and a marking unit at a lower level via a wireless network; the storage module is interactively connected to a capture module, a decomposition module, a hiding module, an analysis module and a determination module at a lower level via a wireless network; the determination module is interactively connected to a sniffing unit at a lower level via a wireless network; and the determination module is interactively connected to a feedback module via a wireless network.

Images