Multi-body-type adaptive human body measurement and personalized garment template generation method

Abstract

The invention belongs to the technical field of garment customization platemaking, and relates to a multi-body-type adaptive human body measurement and personalized garment template generation method, which comprises the following steps: firstly, acquiring human body three-dimensional scanning data, and fitting the acquired data through a parameterized human body model; then, according to preset measurement item characteristics, automatically measuring human body critical dimension data, and according to the human body critical dimension data, generating a basic garment template prototype; adjusting template style design parameters according to design requirements, and generating clothing templates of corresponding styles; and finally, verifying the effect of the generated garment template by using a three-dimensional virtual fitting technology, and performing multiple rounds of parameter optimization and adjustment according to a virtual fitting result until the garment template achieves an expected effect, storing the generated garment template and related data files thereof, and completing the generation process of the personalized garment template. According to the invention, the automatic and high-precision measurement of the sizes of multi-body-type human bodies can be realized, and personalized garment templates can be quickly generated in batches.

Description

Technical Field

[0001] The invention belongs to the technical field of customized clothing pattern making, and relates to a method for generating human body measurements and personalized clothing samples suitable for multiple body types. Background Art

[0002] With the continuous growth of the demand for personalization and customization in the modern consumer market, the clothing industry is undergoing a profound transformation from large-scale standardized production to flexible manufacturing and personalized customization. When consumers choose clothing, in addition to paying attention to fashion trends and style designs, they pay more attention to the comfort, fit and personalized expression of clothing. Therefore, how to improve production efficiency and design accuracy while meeting personalized needs has become a key issue that the clothing industry needs to solve urgently. Traditional clothing pattern making methods, such as the proportion method, short-size method, prototype method, etc., still have many limitations in practical applications and are difficult to meet the diversified and refined needs of today's market.

[0003] First, the traditional pattern-making process relies on manual operation, which is inefficient and has a long design cycle. This pattern-making method not only fails to respond quickly to market demand, but also leads to rising production costs, weakening the market competitiveness of clothing products. At the same time, the manual measurement and pattern-making process are susceptible to human errors, especially when dealing with dynamic human bodies or special body shapes. The accuracy of pattern-making is difficult to guarantee, which in turn affects the fit and comfort of clothing. For example, the prototype method requires the operator to manually measure the human body size, and it is impossible to achieve full process automation. In addition, the clothing prototypes mentioned in Reference 1 (Research on Eastern and Western Women's Clothing Prototypes [J]. Shandong Textile Science and Technology, 2018, 59(03): 6-9.), Reference 2 (Research Progress and Trends of Prototypes of Clothing for Elderly Women [J]. Progress in Textile Science and Technology, 2022, (05): 44-49.) and CN117652742A are mostly based on statistical data of specific human bodies, and only use bust, waist and hip circumference as the basis for universal estimation, which is difficult to meet the personalized customization needs of special body shapes.

[0004] In recent years, with the rapid development of 3D human body scanning technology, clothing pattern making methods based on human body data have received widespread attention. 3D scanning technology can quickly obtain accurate geometric information on the human body surface, providing detailed size data and body shape feature analysis support for clothing pattern making. However, the application of 3D human body scanning technology in the clothing industry is still in the exploratory stage and there are many technical bottlenecks. In the processing process of existing 3D human body scanning data, there is generally a lack of efficient and accurate human body measurement algorithms, making it difficult to automatically extract key dimensions of multiple postures and special body shapes, resulting in the clothing pattern making process not yet being fully automated.

[0005] For example, the method proposed in Document 3 (Research on the Generation Rules of Individualized Young Women's Garment Patterns Based on Local Feature Analysis [D]. Soochow University, 2014) limits the posture of body scanning, and the measurement features need to be manually defined, resulting in certain subjective biases. In addition, similar to the prototype method, some sizes are still obtained by regression fitting of the overall measured population data, lacking the ability to generate personalized and automated patterns. The prototype patterns generated by this method have fixed styles and cannot achieve automated adjustment, addition, or combination of components according to body characteristics and style design requirements, with relatively limited customization capabilities. At the same time, the generated patterns do not support automated virtual fitting functions, making it difficult to further optimize and adjust the design through quick virtual fitting effects.

[0006] Patent CN110163728B proposes a method for extracting human body features based on horizontal cross-section layer cutting. This method requires the correction and regularization of the scanned human body data, which may interfere with the accurate acquisition of real human body sizes to a certain extent. In addition, when dealing with different postures or special body types (such as a bent body type), it is difficult to accurately extract feature data. For example, when the human body is bent, the real chest circumference line is as shown in Figure 1 (a), while the chest circumference line extracted by horizontal cross-section layer cutting is as shown in Figure 1 (b), and the difference between the two is large, unable to accurately reflect the actual size of the human body.

[0007] Document 4 (Research and Application of Personalized Garment Pattern Generation Method [J]. Shanghai Textile Science & Technology, 2020, 48(06): 5 - 7 + 22.) and Patent CN108634459B, etc. propose a method for generating garment patterns based on the flattening of the human body surface. However, due to the complexity of the human body surface geometry, distortion and deformation will inevitably be introduced during the surface flattening process, resulting in uncontrollable deformation of the generated garment patterns. The calculation results of different flattening algorithms may vary significantly, thus affecting the consistency of pattern generation. In addition, although this type of method generates garment patterns based on the human body surface, during actual wearing, the clothing will not completely fit the human body surface, especially the clothing form in specific parts such as the female chest. Extracting highly fitting patterns suitable for tight-fitting clothing from the flattening results usually requires a large amount of manual post-processing operations, and this manual adjustment makes the generated tight-fitting clothing patterns difficult to adapt to further automated adjustments, restricting the diversity and flexibility of garment style pattern design, and thus limiting the application scope of this method.

[0008] Therefore, it is of great significance to study a method for human body measurement and personalized garment pattern generation that can adapt to multiple body types to solve the problems existing in the prior art. Summary of the Invention

[0009] The objective of the present invention is to solve the problems existing in the prior art and provide a method for anthropometric measurement and personalized clothing pattern generation that is adaptable to multiple body types.

[0010] To achieve the above objective, the technical solution adopted by the present invention is as follows:

[0011] A method for anthropometric measurement and personalized clothing pattern generation that is adaptable to multiple body types. First, obtain the three-dimensional human body scan data and fit the obtained three-dimensional human body scan data through a parametric human body model; then, automatically measure the key body dimension data according to the preset measurement item characteristics, and generate a basic clothing pattern prototype based on the key body dimension data; next, adjust the pattern style design parameters according to the design requirements to generate clothing patterns of corresponding styles; finally, use three-dimensional virtual fitting technology to verify the effect of the generated clothing patterns, and based on the virtual fitting results, perform multiple rounds of parameter optimization and adjustment until the clothing patterns reach the expected effect, and then store the generated clothing patterns and their related data files to complete the generation process of personalized clothing patterns;

[0012] Automatically measuring the key body dimension data according to the preset measurement item characteristics means accurately measuring the dimension data of the length, girth, distance, and angle of the parametric human body model by registering the three-dimensional human body with the parametric human body model and combining the preset joint points, surface partition dictionaries, and measurement feature point definitions.

[0013] Generating a basic clothing pattern prototype based on the key body dimension data means designing a pattern generation rule that can dynamically allocate darts based on the measured key body dimension data, and based on the designed pattern generation rule, using a pattern generation system to generate a basic clothing pattern prototype and a parameter configuration file to support subsequent style adjustments.

[0014] Existing clothing pattern-making technologies usually only use a small number of dimensions such as chest circumference and waist circumference as basic parameters, and the dimensions of other parts are calculated through empirical formulas or statistical data. This simplified design method based on limited human body dimensions, although having a certain feasibility in the application of standard body types, is difficult to accurately reflect individual body type differences. Especially for people with non-standard body types, their fit and comfort often cannot be effectively guaranteed. However, the present invention can automatically integrate a variety of measurement data, including multiple data of length, distance, girth, and angle. Based on the professional design specifications of clothing industrial patterns, it intelligently adjusts the dimension parameters and dart distribution of clothing prototypes, and finally generates clothing patterns that highly match individual body type characteristics. Experiments show that the patterns generated by the method of the present invention have a significant improvement in the fit index compared with the prior art, can not only more accurately reflect personalized human body characteristics, but also meet the dual requirements of accuracy and efficiency in modern clothing industrial production.

[0015] The sample generation system includes sample generation rules, domain-specific programming languages, various algorithms, interfaces, etc. Existing technologies directly apply the sample generation rules in the GarmentCode sample generation system to generate clothing samples and conduct 3D virtual fitting. However, there are obvious technical defects in the existing sample generation rules in the GarmentCode sample generation system, which are analyzed as follows:

[0016] 1. Problem of data dimension limitation:

[0017] The existing sample generation rules are based on static human body size measurement data with limited dimensions, resulting in the technical defect that the generated clothing samples have insufficient adaptation to 3D human body characteristics. Especially in the simulation of dynamic human body movement states and application scenarios of special body types, the matching accuracy deviation between the generated samples and the real human body surface is significant, and it cannot meet the requirements of modern clothing industrial production.

[0018] 2. Problem of intelligent dart allocation defect:

[0019] The current rules lack a dynamic dart allocation mechanism based on human body morphological characteristics. Its fixed parameterized dart generation mode leads to inaccurate dart quantity calculation and unbalanced spatial distribution. For example, the darts in the sample may be too small. This technical defect severely restricts the application value of the automated sample system in high-end customization and large-scale flexible production.

[0020] The present invention introduces 3D human body scanning technology, fits the human body scanning data into a parameterized human body model, and extracts multi-dimensional human body size data from it. This method can not only accurately reflect static human body characteristics but also simulate dynamic movement states and special body type scenarios, thus realizing more comprehensive and refined human body measurement. On this basis, the present invention expands the data dimensions relied on in the sample generation rules, significantly improves the adaptability of the generated samples to 3D human body characteristics, and solves the technical defect caused by data dimension limitation; the present invention makes an adaptive adjustment to the original sample generation rules and introduces a dynamic dart allocation mechanism based on human body morphological characteristics. This mechanism can intelligently calculate and reasonably allocate the position and size of the darts according to multi-dimensional key human body size data, ensuring accurate and balanced dart distribution. In addition, based on the improved generation system, the present invention first generates personalized prototype samples and then further generates personalized samples that meet specific style requirements, thereby greatly improving the flexibility and adaptability of sample generation.

[0021] As a preferred technical solution:

[0022] A method for human body measurement and personalized clothing sample generation with multi-body type adaptation as described above specifically includes the following steps:

[0023] Step 1: Use a three-dimensional human body scanning device to collect high-precision human body data of the user, and use data processing software to preprocess the collected data. At the same time, record the clothing thickness information when the user is scanned, and export to obtain a three-dimensional scanned human body mesh model;

[0024] Step 2: Select a parametric human body model, and register the parametric human body model with the three-dimensional scanned human body mesh model in Step 1 (using the mesh registration algorithm of the existing technology for registration, such as the mesh registration algorithm disclosed in Document 5 (Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image[C], ECCV 2016.)); The selected parametric human body model has virtual joints for driving pose adjustment, and the body shape and actions can be controlled by a set of parameters, and has learned the association between human body shape and pose changes through pre-training;

[0025] Step 3: According to the clothing thickness information recorded in Step 1 and the preset distance between the clothing and the human body, perform overall scaling adjustment on the parametric human body model registered with the three-dimensional scanned human body mesh model, and save the data of the parametric human body model before and after adjustment;

[0026] Step 4: Call the joint points, surface partition dictionaries and measurement feature point definitions of the parametric human body, apply various measurement algorithms, and automatically measure and record various key dimensions of the human body, including length, girth, distance and angle;

[0027] Step 5: After verifying the integrity of the dimension information, use the dimension data obtained in Step 4 as the constraint conditions for the clothing prototype. The pattern generation system automatically adjusts the dimensions and dart positions in the pattern generation process according to the constraint conditions of the clothing prototype, and generates and saves the personalized clothing prototype pattern and parameter configuration file;

[0028] Step 6: According to the clothing style design requirements, perform custom adjustment on the personalized clothing prototype pattern parameter configuration file obtained in Step 5, and use the pattern generation system to generate a personalized style pattern and its parameter configuration file;

[0029] Step 7: Perform automated virtual fitting on the personalized style pattern generated in Step 6, repeatedly adjust the pattern design according to the fitting effect until the expected effect is achieved, and finally save the personalized style pattern of the clothing and its related files.

[0030] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. In step 1, the 3D human body scanning device needs to be able to acquire full-body data, requiring that the proportion of defective areas caused by occlusion does not exceed 10%, and the scanning accuracy error of the 3D human body scanning device is within ±5 mm, and it needs to meet the standard GB / T 23698-2023 (General requirements for 3D scanning anthropometric methods).

[0031] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. There will be many defects in the original data collected by the scanning device and need to be preprocessed. The preprocessing in step 1 includes but is not limited to mesh denoising, defect repair, and retopology processing.

[0032] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. In step 2, the selected parametric human body model is the SMPL parametric human body model. The SMPL parametric human body model is from the literature 6 (SMPL: A skinned multi-person linear model[M] / / Seminal Graphics Papers: Pushing the Boundaries, Volume 2. 2023: 851-866.).

[0033] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. After the registration in step 2, except for the head region, the chamfer distance between the parametric human body model and the 3D scanned human body mesh model is less than 5 mm. The calculation formula for the chamfer distance is:

[0034] ;

[0035] where is and 's chamfer distance, and are the vertex sets of the parametric human body model and the 3D scanned human body mesh model respectively, and represent a single vertex of the parametric human body model and the 3D scanned human body mesh model respectively, is the point to the point 's Euclidean distance, and are respectively and the number of vertices in.

[0036] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. In step 3, the overall scaling of the parameterized human model registered with the 3D scanned human mesh model is controlled by the surface offset distance. The algebraic formula for the surface offset distance between the scaled model and the original model is:

[0037] ;

[0038] where, is the surface offset distance between the scaled model and the original model, is the preset distance between the clothing and the human body, is the clothing thickness recorded in step 1.

[0039] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. In step 4, the surface partition dictionary consists of the names of each part of the parameterized human model and the corresponding face indices; the measurement feature points are the vertices on the surface of the parameterized human model, or the spatial coordinate average of two surface vertices.

[0040] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. In step 5, verifying the integrity of the size information means checking the integrity of the human body size measurement data to ensure that it meets the input conditions required for generating the personalized clothing prototype pattern.

[0041] A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. In step 7, according to the spatial arrangement and connection relationship definition of the personalized style pattern, perform virtual sewing simulation operations to achieve the virtual fitting effect.

[0042] The virtual fitting technology used is from reference 7 (Warp: A high-performance python framework for gpu simulation and graphics[C] / / NVIDIA GPU Technology Conference (GTC). 2022.).

[0043] Principle of the invention:

[0044] The present invention discloses a method for human body measurement and personalized clothing pattern generation for multi-body type adaptation, which effectively solves problems such as insufficient accuracy of human body measurement and poor pattern adaptability in traditional clothing customization through innovative technical solutions. In the human body measurement link, aiming at problems such as strict requirements for the posture of the measured person and large measurement errors caused by relying on manual marking in the existing technology, this method adopts a technical solution combining high-precision three-dimensional human body scanning and intelligent grid registration. By accurately aligning the obtained three-dimensional scanning data with a pre-trained parametric human body model, automatic adaptation to different body types and various postures is achieved. Combined with a standardized intelligent measurement algorithm, the automation level and measurement accuracy of human body size extraction in multi-posture scenarios are significantly improved.

[0045] In terms of clothing pattern generation technology, the present invention overcomes the limitations of the existing technology that only designs patterns based on length and girth dimensions and has an unreasonable dart distribution. By constructing a multi-dimensional measurement system including four major categories of parameters: length, girth, distance, and angle, comprehensive capture of human body morphological characteristics is achieved. Based on the geometric mapping relationship between the human body morphology and the clothing pattern, the system can automatically optimize the size parameters and dart distribution scheme of the clothing prototype, and generate a clothing prototype pattern that precisely matches the individual body type. On this basis, through a parametric adjustment mechanism, the pattern is optimized in style, significantly improving the accuracy of pattern design. In particular, the pattern generated by this method strictly follows the GarmentCode specification, provides a highly adjustable parametric interface, and supports intelligent adjustment through a clothing style parameter configuration file, realizing the automatic virtual sewing function. This technical solution not only greatly improves the automation level and production efficiency of the pattern-making process, but also further ensures the accuracy and applicability of pattern design through deep integration with virtual fitting technology.

[0046] The technical advantages of the present invention are mainly reflected in: First, through the combination of three-dimensional scanning and automatic measurement algorithms, high-precision and automation of human body measurement are achieved; second, the pattern generation method based on a multi-dimensional parameter system significantly improves the fit of clothing; finally, the parametric design interface provides flexible technical support for personalized customization. This technical solution provides an efficient and accurate solution for the field of clothing personalized customization and has important promotion and application value.

[0047] Beneficial effects:

[0048] (1) A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation. By using 3D body scanning technology to generate a parametric human body model, multi-dimensional size data is extracted, which can accurately reflect static characteristics and simulate dynamic movements and special body types, achieving more precise anthropometric measurement. On this basis, the data dimension of the pattern generation rules is extended to improve the adaptability of the pattern to 3D human body characteristics and solve the problem of data limitations. At the same time, a dynamic dart distribution mechanism is introduced to intelligently optimize the position and size of darts to ensure reasonable and balanced distribution. The improved system first generates personalized prototype patterns and then generates specific style patterns, greatly enhancing the flexibility and adaptability of pattern generation.

[0049] (2) A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation can adapt to various human body type differences and achieve automatic measurement of key human body dimensions. Compared with traditional methods that rely on manual marking and manual measurement, the present invention eliminates the influence of human errors, significantly improves the accuracy and consistency of measurement results, simplifies the operation process, reduces the technical usage threshold and time cost, ensures the high efficiency and practicality of the system, and provides high-precision data support for subsequent clothing pattern generation.

[0050] (3) A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation adopts an adaptive pattern generation system that can quickly generate personalized prototype patterns highly matching human body dimensions and has high customization flexibility, supporting various style changes and virtual fitting requirements. By combining clothing style configuration files with virtual fitting technology, the present invention realizes rapid optimization and adjustment of clothing patterns, provides a highly personalized style design function, and fully meets the requirements of modern clothing design for quick response and high-quality output.

[0051] (4) A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation not only significantly improves the production efficiency and large-scale application ability of personalized clothing customization, but also breaks through the limitations of existing technologies in terms of human body size extraction accuracy, pattern-making efficiency, and automation degree, providing an efficient and accurate overall solution for modern personalized clothing customization, and providing strong technical support for the continuous innovation and high-quality development of the clothing industry in the fields of intelligent design, rapid production, and personalized customization, with broad application prospects and significant industrial value. Description of the Drawings

[0052] Figure 1 It is a comparison diagram of the actual chest circumference line and the chest circumference line extracted by horizontal section layer cutting; among them, (a) is the actual chest circumference line when the human body bends, and (b) is the chest circumference line extracted by horizontal section layer cutting when the human body bends.

[0053] Figure 2Schematic diagram of joint points of parametric human body model;

[0054] Figure 3 Schematic diagram of partitioning of parametric human body model; among which, (a) is the front partitioning schematic diagram of parametric human body model, and (b) is the back partitioning schematic diagram of parametric human body model;

[0055] Figure 4 Schematic diagram of measurement feature points of parametric human body model; among which, (a) is the front measurement feature point schematic diagram of parametric human body model, and (b) is the back measurement feature point schematic diagram of parametric human body model;

[0056] Figure 5 Schematic diagram of measurement items in specific implementation manner; among which, (a) is the front measurement item schematic diagram of human body, and (b) is the back measurement item schematic diagram of human body;

[0057] Figure 6 Process I of drafting prototype of upper garment; among which, (a) is the drafting result of prototype frame line of the back half piece of upper garment, and (b) is the drafting result of prototype frame line of the front half piece of upper garment;

[0058] Figure 7 Process II of drafting prototype of upper garment; among which, (a) is the process result of drafting darts of the back half piece of upper garment prototype, and (b) is the process result of drafting darts of the front half piece of upper garment prototype;

[0059] Figure 8 Process III of drafting prototype of upper garment; among which, (a) is the drafting result of prototype arc line of the back half piece of upper garment, and (b) is the drafting result of prototype arc line of the front half piece of upper garment;

[0060] Figure 9 Schematic diagram of drafting prototype of sleeve;

[0061] Figure 10 Comparison diagram of prototype and personalized clothing pattern; among which, (a) is the comparison diagram of the back half piece of upper garment pattern, (b) is the comparison diagram of the front half piece of upper garment pattern, and (c) is the comparison diagram of sleeve pattern;

[0062] Figure 11 Virtual fitting result of prototype and personalized clothing pattern; among which, (a) is the fitting result of upper garment prototype and sleeve prototype, and (b) is the schematic diagram of the finally generated personalized clothing pattern after adjustment.

[0063] Figure 12 Fitting result of the clothing tried on with the pattern generated according to the new Nihon style prototype rule; among which, (a) is the front view of the visualization of clothing deformation, (b) is the side view of the visualization of clothing deformation, and (c) is the rear view of the visualization of clothing deformation.

[0064] Figure 13The fitting result of the sample garment generated by the built-in rules of GarmentCode; among them, (a) is the front view of the visualized garment deformation, (b) is the side view of the visualized garment deformation, and (c) is the rear view of the visualized garment deformation.

[0065] Figure 14 The fitting result of the sample garment generated by the method of the present invention; among them, (a) is the front view of the visualized garment deformation, (b) is the side view of the visualized garment deformation, and (c) is the rear view of the visualized garment deformation.

[0066] Figure 15 The waist cross-sectional views of different fitting results: among them, (a) is the new cultural prototype rule, (b) is the built-in rule of GarmentCode, and (c) is the rule of the present invention.

[0067] Figure 16 The overall flowchart of a method for anthropometric measurement and personalized garment pattern generation with multi-body type adaptation of the present invention. Detailed implementation manners

[0068] The present invention will be further described below in conjunction with the detailed implementation manners. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0069] A method for anthropometric measurement and personalized garment pattern generation with multi-body type adaptation, as Figure 16 shown, the specific steps are as follows:

[0070] Step 1: Use a three-dimensional human body scanning device to collect high-precision human body data of the user, and use data processing software to preprocess the collected data. At the same time, record the clothing thickness information when the user is scanned, and export to obtain a three-dimensional scanned human body mesh model;

[0071] Among them, the scanning accuracy error of the three-dimensional human body scanning device is within ±5 mm; the preprocessing includes mesh denoising, defect repair and retopology processing; the model after preprocessing needs to meet the following requirements: adopt a triangular mesh structure, with uniform vertex position distribution, and the number of vertices is not less than 500; there is no non-manifold boundary or self-intersection phenomenon in the mesh structure.

[0072] Step 2: Select an SMPL parametric human body model, and register the parametric human body model with the three-dimensional scanned human body mesh model in Step 1; the selected parametric human body model has virtual joints for driving pose adjustment, and the body type and movement can be controlled by a set of parameters, and has learned the correlation between human body shape and pose changes through pre-training.

[0073] After registration, except for the head region, the chamfer distance between the parametric human body model and the three-dimensional scanned human body mesh model is less than 5 mm. The calculation formula for the chamfer distance is:

[0074] ;

[0075] where is and 's chamfer distance. and are the vertex sets of the parametric human body model and the three-dimensional scanned human body mesh model respectively. and represent a single vertex of the parametric human body model and the three-dimensional scanned human body mesh model respectively. is the point to the point 's Euclidean distance. and are respectively and the number of vertices in.

[0076] Step 3: According to the clothing thickness information recorded in Step 1 and the preset clothing-to-human body spacing distance, the parametric human body model registered with the three-dimensional scanned human body mesh model is adjusted for overall scaling by the surface offset distance, and the data of the parametric human body model before and after the scaling adjustment is saved; the parametric human body model before the scaling adjustment is used for the subsequent realization of virtual fitting, while the parametric human body model after the scaling adjustment is used for the measurement of key human body dimensions in Step 4; the saved data of the parametric human body model includes the definition file of body type and motion parameters and the corresponding mesh model file;

[0077] The algebraic expression for the surface offset distance between the scaled model and the original model is:

[0078] ;

[0079] where is the surface offset distance between the scaled model and the original model, is the preset clothing-to-human body spacing distance, is the clothing thickness recorded in Step 1;

[0080] The spatial coordinate system of the parametric human body model is defined as follows: The direction of the anterior head surface (the plane parallel to the forehead and perpendicular to the sagittal plane) is set as the axis of the coordinate system, the center of the left-right equal division of the human body is set as the axis origin, the up-down direction is defined as the axis, the ground direction is the axis origin, and the sagittal direction is defined as Axis, with the center of the front-back equal division of the human body set as the origin of the axis;

[0081] Step 4: Call the joint points, surface partition dictionary, and measurement feature point definitions of the parametric human body, apply various measurement algorithms, and automatically measure and record various key dimensions of the human body, including length, circumference, distance, and angle;

[0082] The surface partition dictionary consists of the names of each part of the parametric human body model and the corresponding face indices; the face index is used to uniquely identify each face in the mesh model, and its index is a number that increases incrementally starting from 0; each face consists of three vertex indices;

[0083] The measurement feature points are the vertices on the surface of the parametric human body model, or the spatial coordinate average value of two surface vertices; the vertex index is used to uniquely identify each vertex in the mesh model, and its index also starts from 0 and increases incrementally, and corresponds one-to-one with the spatial coordinates of the vertex; the spatial coordinates of the vertex represent the position of the vertex in three-dimensional space, and each vertex is defined by (x, y, z) coordinate information.

[0084] Both the surface partition dictionary and the measurement feature point data are stored in the template generation system and can be repeatedly called under the condition of the same parametric human body model. The automatic measurement methods for various categories are as follows:

[0085] Length dimension: According to the predefined measurement item definition, select two feature points, calculate the Euclidean distance between the two points in three-dimensional space, and use this as the length dimension of the current measurement item. The calculation formula for the Euclidean distance is:

[0086] ;

[0087] Wherein, is the length dimension of the current measurement item, and are the three-dimensional spatial coordinates of feature point 1 and feature point 2.

[0088] Circumference dimension: According to the predefined measurement item definition, select one feature point and two joint points to determine a cutting plane. The origin of the cutting plane is set as the spatial coordinates of the feature point, and its normal direction is determined by the vector formed by the two joint points, where the first joint point is the starting point of the vector and the second joint point is the end point of the vector. Use the cutting plane to intercept the parametric human body model. Considering that the human body shape and movement changes may cause multiple cross-sections in local areas, the obtained cross-sections are screened according to the human body partition dictionary to determine the only valid cutting cross-section. Perform three-dimensional convex hull processing on the boundary of the valid cutting cross-section to generate a three-dimensional convex hull cutting cross-section. Calculate the sum of the lengths of all sides of the three-dimensional convex hull cutting cross-section and subtract the length of the closed side, and use this as the circumference dimension of the current measurement item.

[0089] Distance dimension: According to the definition of the preset measurement item, two feature points are selected, and the geodesic distance between the two feature points on the surface of the parametric human body model is calculated, which is used as the distance dimension of the current measurement item.

[0090] Angle dimension: According to the definition of the preset measurement item, the target feature point is selected as the vertex of the measurement angle, and this vertex is set as the starting point of the vector. Further, two other measurement feature points are selected as the ending points of the vector, and two measurement angle vectors are respectively constructed. Based on the two measurement angle vectors, the included angle value is calculated in the three-dimensional space, which is used as the angle dimension of the current measurement item.

[0091] Among them, the measurement accuracy error of the length, girth, and distance categories is ±1 mm, and the measurement accuracy error of the angle category is ±0.1°.

[0092] Step 5: Verify the integrity of the size information: That is, verify the integrity of the human body size measurement data to ensure that it meets the input conditions required for generating the personalized clothing prototype pattern. If the measurement data does not meet the preset requirements, the system will trigger an error prompt and interrupt the subsequent operations; after verifying the integrity of the size information, use the size data obtained in Step 4 as the clothing prototype constraint conditions, and the pattern generation system automatically adjusts the size and dart position during the pattern generation process according to the clothing prototype constraint conditions, and generates and saves the personalized clothing prototype pattern and the parameter configuration file;

[0093] In the pattern generation system, parametric prototype generation models for various clothing categories such as upper garments, sleeves, skirts, and trousers are preset. By default, each prototype pattern consists of the front and back pieces of the right half of the human body, and the left pattern is obtained by symmetry with the right pattern. For the situation where there are large differences in the key sizes of the left and right sides of the human body or other special requirements, an independent left prototype pattern can be generated. The size of the left prototype pattern is generated based on the left human body measurement data, and the surface partition dictionary and feature points used in the left and right human body measurement items follow the principle of symmetry about the median sagittal plane.

[0094] Step 6: According to the clothing style design requirements, customize and adjust the personalized clothing prototype pattern parameter configuration file obtained in Step 5, and use the pattern generation system to generate the personalized style pattern and its parameter configuration file;

[0095] The template generation system should support calling configurable parameter templates, allowing direct modification and writing of related configurations. It is not only necessary to have the function of parametric adjustment of the basic dimensions of the prototype, but also to provide a flexible personalized adjustment interface to support automatic adjustment based on human body size and style requirements. The adjustment content includes but is not limited to design elements such as the number of darts and the shape of the armhole line. According to the measurement data obtained in step 4 and the design requirements of the template structure, the value range of the template parameters needs to be reasonably set to avoid self-intersection at the edges of the template and ensure the feasibility and stability of the design. At the same time, the generated template should have an automated virtual fitting function, defining the connection relationship between the templates and their placement in three-dimensional space, so that users can intuitively evaluate the design effect and make further optimizations and modifications based on feedback.

[0096] The template generation system should have built-in component definitions that are compatible with the template generation system in step 4, including but not limited to various neckline shapes, armhole shapes, cuff shapes, slit shapes, etc. Users can modify, add or combine component design features in the parameter configuration file within the set value range, and use the template generation system to regenerate the template, thereby changing the shape and style of the template.

[0097] Step 7: According to the spatial arrangement and connection relationship definition of the personalized style sample generated in step 6, perform virtual sewing simulation operations to achieve automated virtual fitting effects, and then repeatedly adjust the sample design according to the fitting effect until the expected effect is achieved. Finally, save the personalized style sample of the clothing and its related files. The files should include the sample vector graphic format, parameter configuration file and virtual fitting related data files.

[0098] The following uses a specific embodiment to illustrate a method for generating a human body measurement and personalized clothing sample for multiple body types according to the present invention, taking the generation of a V-neck T-shirt sample with a loose waist as an example, as follows:

[0099] (1) The subject wears 1mm body-fitting clothing and stands in a fixed position on the ground and assumes a specific posture. The complete human body data is obtained through a handheld 3D infrared light scanner. The basic mesh model is preprocessed using Magic3D software, and the 3D scanned human body mesh model file is exported in obj format.

[0100] (2)Select the SMPL parametric human model. The SMPL parametric human model is jointly determined by 10-dimensional shape parameters, 72-dimensional pose parameters, and 3-dimensional translation parameters to determine the shape, pose, and position of the human body. Apply the mesh registration algorithm to iteratively optimize the shape, pose, and translation parameters of the model to match the scanned human mesh model. The number of iterations of the mesh registration algorithm is set to 500, the learning rate is set to 0.1, the starting number of learning rate decays is 450, and the normal vector threshold angle is 30°. After the human pose and action matching are completed, the chamfer distance between the scanned mesh and the parametric human mesh is 0.32 mm, meeting the set requirements. Save the parameter definition file in json format and the mesh model file in obj format of the parametric human model, and use this human mesh model during the fitting process.

[0101] (3)According to the clothing thickness information recorded in step (1) and the preset distance between the clothing and the human body, perform an overall scaling adjustment on the parametric human model registered with the 3D scanned human mesh model through the surface offset distance, and save the data of the parametric human model before and after the adjustment. In the present invention, the preset distance between the clothing and the human body is set to 2.5 mm, and the clothing thickness during human body scanning is 1 mm. Expand the optimized parametric human model outward by 1.5 mm as a whole, and use the expanded human model during measurement;

[0102] (4)Call the parametric human joint points, surface partition dictionary, and measurement feature point definitions, and apply various measurement algorithms to automatically measure various key human body dimensions, including length, girth, distance, and angle categories;

[0103] The joint points of the parametric human model are as Figure 2 shown, including B01 head, B02 neck, B03 right shoulder, B04 right elbow, B05 right wrist, B06 left shoulder, B07 left elbow, B08 left wrist, B09 right collarbone, B10 left collarbone, B11 spine 3, B12 spine 2, B13 spine 1, B14 pelvis, B15 right hip, B16 right knee, B17 right ankle, B18 left hip, B19 left knee, and B20 left ankle.

[0104] The adopted surface partition of the human model is as Figure 3 shown, including Z01 head and neck, Z02 right upper arm, Z03 right forearm, Z04 right hand, Z05 left upper arm, Z06 left forearm, Z07 left hand, Z08 front chest, Z09 back, Z10 front abdomen, Z11 back waist and hip, Z12 right thigh, Z13 right calf, Z14 right foot, Z15 left thigh, Z16 left calf, and Z17 left foot. The names of the corresponding positions and the involved triangular faces form the human surface partition.

[0105] The adopted measurement feature point definitions are as Figure 4As shown, the names of the feature points corresponding to the numbers and their human vertex index settings in the specific implementation are shown in Table 1 below. Among them, some feature points can be obtained indirectly by calculating other feature points. For example, M20 and M21, and the calculation method is the average value of the coordinates of the relevant feature points.

[0106] Table Definition Table of Measured Feature Points in the Specific Implementation

[0107]

[0108] The defined items of the preset length dimension measurement are shown in Table 2. Calculate the Euclidean distance between two feature points in the three-dimensional space, and use this as the length dimension of the current measurement item.

[0109] Table 2 Definition Table of Length Dimension Measurement Items in the Specific Implementation

[0110]

[0111] The defined items of the preset girth dimension measurement are shown in Table 3. First, determine a specific cutting plane, the origin of which is set to the spatial coordinates of Feature Point 1, and its normal direction is determined by the vector formed by two joint points, where Joint Point 1 is the starting point of the vector and Joint Point 2 is the ending point of the vector. Perform an intercepting operation on the parametric human model based on the above cutting plane. Given that the human body shape and movement changes may result in multiple cross-sections in a local area, screen the obtained cross-sections according to the human body partition dictionary to determine the only valid cutting cross-section. Perform a three-dimensional convex hull processing on the boundary of the effective cutting cross-section to generate a three-dimensional convex hull cutting cross-section, and further calculate the sum of the lengths of all sides of this three-dimensional convex hull cutting cross-section. Since the convex hull edges intercepted by the partition will automatically close, it is necessary to subtract the length of the closed edge from the sum of all side lengths, and use this as the girth dimension of the current measurement item.

[0112] Table 3 Definition Table of Girth Dimension Measurement Items in the Specific Implementation

[0113]

[0114] The defined items of the preset distance dimension measurement are shown in Table 4. Calculate the geodesic distance between two feature points on the surface of the parametric human model, and use this as the distance dimension of the current measurement item.

[0115] Table 4 Definition Table of Distance Dimension Measurement Items in the Specific Implementation

[0116]

[0117] The predefined angular dimension measurement items are defined as shown in Table 5. Select feature point 1 as the vertex of the measurement angle, and set this vertex as the starting point of the vector. Further select feature points 2 and 3 as the ending points of the vector, and construct two measurement angle vectors respectively. Based on the two measurement angle vectors, calculate the included angle value in the three-dimensional space, and use this as the angular dimension of the current measurement item.

[0118] Table 5 Definition Table of Angular Dimension Measurement Items in the Specific Embodiment

[0119]

[0120] In the specific embodiment, it involves the generation of the upper body prototype and the sleeve prototype. The measurement items involved are shown in Figure 5 as shown, and the measurement results are shown in Table 6.

[0121] Table 6 Measured Values of Human Key Dimensions in the Specific Embodiment

[0122]

[0123] (5) According to the dimensions and setting rules, use the automated pattern generation system to automatically adjust the dimensions of the clothing prototype and the position of the darts, and generate and save the personalized clothing prototype pattern and parameter setting files, including the upper body prototype and the sleeve prototype; the parameter configuration file includes the part style and the default values and value ranges of the part dimensions. Among them, the default style value is the selected style name, and the value range is each alternative style name; the default dimension value is based on the measured dimension, set to 1, and the value range is a specific multiple of the default dimension. For example, 0.8~1.2 is 0.8 times to 1.2 times of the default dimension. The user can select a specific part name or dimension multiple within the part style library and dimension value range according to the style needs to change the style design of the generated pattern.

[0124] As Figures 6 - 9 shown, the drafting steps of the upper body and sleeve prototypes are as follows:

[0125] (5.1) Draw the front half-frame line;

[0126] The width of the front half-frame line . According to L02 and L06, draw the bust line and mark the position of the bust point M08. The bust point is on the bust line, and the horizontal distance from the front center line is L06. Combine D01 and , and determine the height of the front half-frame. According to D01 and D02, determine the shape of the shoulder slope line. Extend the shoulder slope line to the front side base line FSL to form the front half-shoulder slope line.

[0127] (5.2) Draw the back half-frame line;

[0128] The width of the back half-frame line is According to D05 and D06, determine the position of the posterior underarm point M18 in the pattern. Combine D07 and , and determine the height of the rear half-frame. According to D07 and D08, determine the shape of the rear half-shoulder slope line and extend it to the rear side base line BSL to form the rear half-shoulder slope line.

[0129] (5.3) Front half bust dart design;

[0130] The bust dart size is calculated based on the bust dart angle and the bust dart width. Among them, the bust dart angle , and the bust dart width . Due to the addition of the bust dart, the basic side seam line tilts forward towards the front center, and the tilt angle is . The offset distance of the midpoint of the bust dart setting from the lower end point of the tilted side seam line is .

[0131] (5.4) Front half waist dart design;

[0132] Based on the front half bust-waist difference FD, adjust the waist circumference of the front half. When C01 > C03, distribute the waist darts according to Table 7. Waist dart 1 is located at the vertical projection position of the bust point, and waist dart 2 is located at the midpoint between the vertical projection of the bust point and the side seam line SL. The heights of waist dart 1 and waist dart 2 are . The side dart is formed by offsetting the lower end point of the side seam line forward towards the lower front center. When C01 < C03, no waist darts are added, but the side seam line and the lower front center line are each extended outwards by .

[0133] (5.5) Rear half waist dart design;

[0134] Based on the rear half bust-waist difference BD, adjust the waist circumference of the rear half. When C02 > C04, distribute the waist darts according to Table 7. Waist dart 1 is located at the projection position of the posterior underarm point, and waist dart 2 is located at the midpoint between the projection of the posterior underarm point and the lower back center. The heights of waist dart 1 and waist dart 2 are L02. The side dart is formed by offsetting the lower end point of the side seam line backward towards the lower back center. When C02 < C04, no waist darts are added, but the side seam line and the lower front center line are each extended outwards by .

[0135] Table 7 Rules for distributing waist darts in the upper body prototype

[0136]

[0137] (5.6) Add armhole and neckline arcs;

[0138] Add armhole and neckline arcs in the armhole and neck regions. According to the endpoints and their tangent directions, use the optimization method to generate the armhole and neckline arcs. The specific generation constraint rules are shown in Table 8. The schematic diagrams of the tangent directions of each endpoint are shown in Figure 8The direction of the black arrow in the figure.

[0139] Table 8 Optimization Generation Constraint Rules Table for Armhole and Neckline Curves

[0140]

[0141] (5.7) The drafting steps of the sleeve prototype are as follows:

[0142] Draw the sleeve frame line with a height of D10. According to D09, the armhole curve length, and the tangent direction of the sleeve cap curve, use the optimization method to generate the sleeve cap curve and the sleeve width endpoints. The specific generation constraint rules are shown in Table 9. The schematic diagram of the tangent direction of each endpoint is shown in Figure 9 The direction of the black arrow in the figure.

[0143] Table 9 Optimization Generation Constraint Rules Table for Sleeve Cap Curve

[0144]

[0145] (6) According to the design requirements of the clothing style, if the user selects a loose V-neck T-shirt, then delete the upper body prototype dart definition in the parameter configuration file, select the V-shaped neckline as the default value for the neckline type, and adjust the default value of the sleeve length multiple to 0.3. Use the sample generation system to generate a personalized style sample and its parameter configuration file;

[0146] (7) Conduct virtual fitting on the sample generated in step (6); the user can adjust the parameter setting file of the sample multiple times according to the fitting effect and conduct virtual fitting until the expected effect is achieved. Generate and save the sample file in dxf format, the clothing sample picture file in png format, the virtual fitting three-dimensional model file in obj format, the virtual try-on picture file in png format, and the sample parameter configuration file in json format that meet the AAMA specifications.

[0147] The comparison chart between the prototype and the finally generated personalized clothing sample is as shown in Figure 10 In the figure, the dotted line is the upper body prototype and the sleeve prototype generated in step (4), and the solid line is the sample generated after style adjustment. The virtual try-on effect is as shown in Figure 11 In the figure, Figure 11 (a) is the fitting result of the upper body prototype and the sleeve prototype, Figure 11 (b) is the schematic diagram of the finally generated personalized clothing sample after adjustment.

[0148] To verify the accuracy and effectiveness of the method provided by the present invention, the method of the present invention is compared and verified with the prior art from two aspects: the accuracy of human body measurement and the generation effect of clothing samples.

[0149] (1) Comparison of human body measurement accuracy;

[0150] Referring to the definition of accuracy in the national standard GB / T 23698-2023, that is, the degree to which the measurement result deviates from the true value. In this comparative experiment, the true value refers to the average value measured multiple times by human body measurement experts using traditional measurement instruments (such as tape measures and calipers). The automatic human body measurement results obtained by using the method of the present invention and the automatic human body measurement results obtained by using the prior art are respectively compared with the above-mentioned true value, and the respective relative errors are calculated. Considering that the measurement items supported by the existing automatic measurement technology are usually fewer than those of the method of the present invention, this experiment selects the upper body prototype as an example. Under the condition that the subject wears close-fitting clothing, 7 key measurement items (chest circumference, waist circumference, back length, front chest width, chest point distance, shoulder length, and chest point-side neck point distance) that can be measured by both technologies are selected for comparison. The human body dimensions of the subject are measured respectively in the standard standing posture and running motion (referred to as standing posture and running posture), and the relative errors are calculated and recorded. The specific data are shown in Table 10.

[0151] Table 10 Comparison of measurement relative errors between the prior art and the method of the present invention under different human postures

[0152]

[0153] It can be seen from the data in Table 10 that in the standard standing posture, the measurement relative error of the method of the present invention is generally lower than that of the prior art. The errors of both methods under this static condition are within the range allowed by the national standard. In the running motion, although the measurement relative errors of both methods increase compared with the static state, the relative error of the method of the present invention is significantly lower than that of the prior art, especially in the key dimensions of clothing structure such as chest circumference, waist circumference, and back length, showing better measurement stability and accuracy.

[0154] In summary, the experimental results show that the human body measurement method provided by the present invention has higher accuracy than the prior art, especially in dynamic human body measurement.

[0155] (2) Comparison of the effects of clothing pattern generation;

[0156] To evaluate the fitting effects of the patterns generated by different pattern generation rules, this part uses virtual fitting for qualitative comparison. The comparison objects include: the pattern generation rules built in GarmentCode, the commonly used chest measurement method pattern generation rules (New Culture-style prototype rules) in the prior art, and the pattern generation rules of the present invention.

[0157] The experimental steps are as follows: For a specific three-dimensional virtual human body model, the above three rules are respectively applied to generate the basic patterns of the upper body. Subsequently, in the professional clothing simulation software CLO3D, the same fabric properties, sewing relationships, and simulation parameters are set, and the generated patterns are virtually tried on. Through the visual clothing deformation map and observing the static form of the clothing worn on the virtual mannequin (such as Figures 12 - 14As shown, where the red area usually indicates stress concentration or large deformation, indicating that there may be problems with the fit in this area), and combined with the cross-sectional view of the waist (such as Figure 15 as shown) to comprehensively evaluate the fit and balance of the pattern.

[0158] For the pattern generated based on the new Japanese prototype rules, since it mainly relies on a few parameters such as chest circumference and back length for grading, for the specific human model adopted in the specific implementation manner, the generated clothing appears too loose. Although the deformation amount on the clothing surface is not large, the overall fit is insufficient. From Figure 15 the cross-sectional view of the waist shown in (a), it can also be observed that the gaps between the clothing and the human body are unevenly distributed front and back, indicating problems with the balance of the body of the clothing.

[0159] For the pattern generated using the built-in rules of GarmentCode, its fit degree is improved compared to the new Japanese prototype. However, in the virtual fitting effect, it can still be observed that the waistline of the back piece is significantly higher than that of the front piece, and there is unnecessary warping at the upper part of the sleeve, and the balance of its body structure is poor. At the same time, in the visualization diagram of the clothing deformation amount, obvious clothing deformations also appear in areas such as the collar, shoulders, and armholes.

[0160] For the pattern generated using the method of the present invention, compared with the above two existing technologies, it shows fewer surface wrinkles after virtual fitting, and the clothing shape can better fit the three-dimensional contour of the human body. The waistline is basically horizontal front and back, and the overall balance of the body of the clothing is better.

[0161] The comprehensive comparison of the experimental results can prove that the pattern generation method provided by the present invention can generate clothing patterns with a higher matching degree with the target human body and a more reasonable dart distribution, thus achieving better clothing fit, appearance effect, and wearing comfort.

Claims

1. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation, characterized in that: First, obtain the three-dimensional human body scan data and fit the obtained three-dimensional human body scan data with a parametric human body model. Then, according to the characteristics of preset measurement items, automatically measure the key body dimension data, and generate a basic clothing pattern prototype based on the key body dimension data. Next, adjust the pattern style design parameters according to the design requirements to generate clothing patterns of corresponding styles. Finally, use the three-dimensional virtual fitting technology to verify the effect of the generated clothing patterns, and according to the virtual fitting results, perform parameter optimization and adjustment until the clothing patterns reach the expected effect. After that, store the generated clothing patterns and their related data files to complete the generation process of personalized clothing patterns; Automatically measuring the key body dimension data according to the characteristics of preset measurement items means accurately measuring the dimension data of the length, circumference, distance, and angle of the parametric human body model by registering the three-dimensional human body with the parametric human body model and combining the defined preset joint points, surface partition dictionaries, and measurement feature points; Generating a basic clothing pattern prototype based on the key body dimension data means designing a pattern generation rule that can dynamically distribute darts based on the measured key body dimension data, and generating a basic clothing pattern prototype using a pattern generation system based on the designed pattern generation rule.

2. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 1, characterized in that, Specifically, it includes the following steps: Step 1: Use a three-dimensional human body scanning device to collect high-precision human body data of the user, and use data processing software to preprocess the collected data. At the same time, record the clothing thickness information when the user is scanned, and export to obtain a three-dimensional scanned human body mesh model; Step 2: Select a parametric human body model and register the parametric human body model with the three-dimensional scanned human body mesh model in Step 1; the selected parametric human body model has virtual joints, and its body shape and movements can be controlled by a set of parameters, and it has been pre-trained to learn the correlation between human body shape and posture changes; Step 3: According to the clothing thickness information recorded in Step 1 and the preset distance between the clothing and the human body, perform an overall scaling adjustment on the parametric human body model registered with the three-dimensional scanned human body mesh model, and save the data of the parametric human body model before and after the adjustment; Step 4: Call the joint points, surface partition dictionaries, and measurement feature point definitions of the parametric human body, apply various measurement algorithms, and automatically measure and record various key body dimensions, including length, circumference, distance, and angle; Step 5: After verifying the integrity of the dimension information, use the dimension data obtained in Step 4 as the constraint conditions for the clothing prototype. The pattern generation system automatically adjusts the dimensions and dart positions in the pattern generation process according to the constraint conditions of the clothing prototype, and generates and saves the personalized clothing prototype pattern and parameter configuration file; Step 6: According to the clothing style design requirements, perform custom adjustments on the parameter configuration file of the personalized clothing prototype pattern obtained in Step 5, and use the pattern generation system to generate a personalized style pattern and its parameter configuration file; Step 7: Perform automated virtual fitting on the personalized style pattern generated in Step 6, and repeatedly adjust the pattern design according to the fitting effect until the expected effect is achieved, and finally save the personalized style pattern of the clothing and its related files.

3. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that, The scanning accuracy error of the 3D human body scanning device in Step 1 is within ±5 mm.

4. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that, The preprocessing in Step 1 includes mesh denoising, defect repair, and retopology processing.

5. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that, The parameterized human body model selected in Step 2 is the SMPL parameterized human body model.

6. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that After the registration in Step 2, except for the head region, the chamfer distance between the parameterized human body model and the 3D scanned human body mesh model is less than 5 mm. The calculation formula for the chamfer distance is: ； Among them, is and 's chamfer distance, and are respectively the vertex sets of the parametric human model and the 3D scanned human mesh model, and respectively represent a single vertex of the parametric human model and the 3D scanned human mesh model, is the point to the point 's Euclidean distance, and are respectively and the number of vertices in.

7. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that, In Step 3, the overall scaling of the parameterized human body model registered with the 3D scanned human body mesh model is controlled by the surface offset distance. The algebraic expression for the surface offset distance between the scaled model and the original model is: ； Among them, is the surface offset distance between the scaled model and the original model, is the preset distance between the clothing and the human body, is the clothing thickness recorded in Step 1.

8. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that, In Step 4, the surface partition dictionary consists of the names of each part of the parameterized human body model and the corresponding face indices; the measurement feature points are the vertices on the surface of the parameterized human body model, or the spatial coordinate average values of two surface vertices.

9. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that Verifying the integrity of the size information in Step 5 means checking the integrity of the human body size measurement data to ensure that it meets the input conditions required for generating personalized clothing prototype templates.

10. A method for anthropometric measurement and personalized clothing pattern generation with multi-body type adaptation according to claim 2, characterized in that, In Step 7, according to the spatial arrangement and connection relationship definition of the personalized style template, perform virtual sewing simulation operations to achieve the virtual fitting effect.

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