CN103886117A - Method for improving virtual human modeling accuracy in 3D clothing fitting software - Google Patents
Method for improving virtual human modeling accuracy in 3D clothing fitting software Download PDFInfo
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Abstract
The invention discloses a method for improving virtual human modeling accuracy in 3D clothing fitting software. The method includes 1, selecting 11 figure modeling parameters affecting surface greatly to serve as BP neural network output parameters; 2, figuring out 36 figure feature parameters to serve as BP neural network input parameters; 3, establishing an initial model of a BP network in Matlab environment, acquiring an optimal network structure by means of network training, and establishing a nonlinear mapping relationship of the figure feature parameters and figure adjusting parameters; 4, analyzing the encoding manner of human parameter modeling interface files in the 3D clothing fitting software, and generating a customized virtual human based on a customized figure and human size. The method has the advantages that the personalized and customized human modeling requirements for clothing fitting evaluation can be met, efficient personalized human body generation from the figure feature parameters can be implemented, and the method can be widely applied to clothing customization platforms.
Description
Technical field
The present invention relates to a kind of method that improves virtual human body modeling accuracy in three-dimensional fitting software, belong to clothes industry digitizing technique field.
Background technology
Along with society and economic development, people start to pursue personalized dress designing, and hope can be worn and be met own physical characteristic and aesthetic customized clothes.Because everyone build, figure and the colour of skin are different, aesthetic standards are also different, so, in current customized clothing designing and making process, need to fit and could observe clothes and wear effect by me.Must be by my trouble of bringing of fitting in order to solve, there is relevant virtual three-dimensional fitting software development at home and abroad, this three-dimensional virtual fitting is the important means for the information analysis of ready-made clothes fitness and evaluation, the quality of three-dimensional virtual fitting effect, the design processing and the specification that directly have influence on clothes are determined link.And wherein, set up according to client's build and size the basis that personalized virtual human model is three-dimensional fitting.In existing three-dimensional fitting software, the modeling of virtual human body personalizing parameters is to realize by girth size and length dimension, then by the subjective judgement of the build to human body, buman body type is finely tuned, the virtual human body generating like this and the difference of actual build are larger, affect the final result of use of three-dimensional fitting.
Summary of the invention
The object of this invention is to provide a kind of method that improves virtual human body modeling accuracy in three-dimensional fitting software, by anthropometry, buman body type characteristic parameter is applied to the research of the mapping relations of the physical characteristic parameter of three-dimensional fitting software, to improve the precision of virtual human body, make the virtual human body generating meet better the fit requirement of evaluating of customization in personalized customization, and by the programmed method analysis of docking port file, to realize the efficient generation from characteristics of human body's parameter to personalized human body, thereby effectively solve the above-mentioned problems in the prior art.
For achieving the above object, the technical solution used in the present invention is as follows:
A method that improves virtual human body modeling accuracy in three-dimensional fitting software, comprises the steps:
The impact of the Human Modeling parameters on human pattern of body form change in the first step, confirmation three-dimensional fitting software, selected 11 affect the output parameter of larger build modeling parameters as BP neural network to body surface;
Second step, according to selected output parameter, analyze 36 physical characteristic parameters that may exert an influence to output parameter, as the input parameter of BP neural network;
The 3rd step, under Matlab environment, build the initial model of BP network, pass through network training, analyze minimax method, variance method for normalizing, and three kinds of methods of non-processor are carried out the impact on neural network forecast ability after standardization to data respectively, analyze the different network numbers of plies impact on neural network forecast ability is set, analyze the impact of different training function setup on neural network forecast ability, analyze different node transport functions the impact on neural network forecast ability is set, analyze different the number of hidden nodes the impact on neural network forecast ability is set, analyze heterogeneous networks input and output parameter the impact on neural network forecast ability is set, obtain optimum network structure, set up physical characteristic parameter and the shape adjustment nonlinearity in parameters mapping relations for three-dimensional fitting software,
The coded system of human parameters modeling interface document in the 4th step, analyzing three-dimensional fitting software, the conversion parameter obtaining according to the 3rd step and corresponding anthropometric data, generate the customization virtual human body based on client's build and human dimension.
As a kind of preferred version, described 11 on the larger build modeling parameters of body surface impact are: shoulder parameter, chest locations parameter, chest peak distance parameter, abdomen parameter, health width parameter, buttocks width parameter, upper part of the body long parameter, pose parameter, lower part of the body pose parameter, bottom position parameter and upper body pose parameter.
As a kind of preferred version, 36 described physical characteristic parameters are: front fillet, rear fillet, average fillet, side neck is put the distance of chest point, side neck is put the distance/front length of chest point, BP dot spacing, BP dot spacing/chest measurement, abdomen coign, Attacking Midfielder's central point is to the angle of chest both sides, side waist central point is to the angle of chest both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of chest both sides to chest both sides, Attacking Midfielder is to the vertical angle of chest, Attacking Midfielder's central point is to the angle of buttocks both sides, side waist central point is to the angle of buttocks both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of buttocks both sides to buttocks both sides, Attacking Midfielder's central point is to the angle of angle/Attacking Midfielder central point chest both sides of buttocks both sides, the angle of side waist central point to angle/side waist central point of buttocks both sides to chest both sides, low back is to the vertical angle of stern, the back of the body is long, back of the body length/height, side knee central point is to the vertical angle of waist central point, side waist central point is to the angle of stern central point, lower body shaft angle, axon angle above the waist, angle, center before upper body, low back is to collare angle, highly, neck circumference, shoulder breadth, chest measurement, under chest, enclose, waistline, lower hip circumference, upper hip circumference, outer leg length and interior length.
As a kind of preferred version, the 3rd step selects minimax method to process data.
As a kind of preferred version, the optimum network structure described in the 3rd step is: the neural network number of plies adopts 4 layers, and the training function setup of network is that { trainlm}, the node transport function of network is set to { logsig, purelin, logsig}.
As a kind of preferred version, described three-dimensional fitting software has build input parameter, shape adjustment parameter and human parameters modeling interface document.
As further preferred version, described three-dimensional fitting software is the V-stitcher software of Gerber company of the U.S..
Compared with prior art, the present invention has following conspicuousness progress and beneficial effect:
The present invention is based on the basis of anthropometry, use BP neural network, set up the Nonlinear Mapping relation of build input parameter in three-dimensional virtual human body girth size, height dimension, length dimension, angle-data and three-dimensional fitting software, adopt characteristics of human body parameter to be converted to be applied to the shape adjustment parameter of three-dimensional fitting software to reach the method that improves virtual human body modeling accuracy.Can not only meet for the virtual fitting personalization of upper garment fit assessment, the Human Modeling demand of customization, and can provide technical support for the customization of clothes industry, the efficient generation from characteristics of human body's parameter to personalized human body can be realized, custom made clothing platform can be widely used in.
Embodiment
Below the present invention is illustrated in further detail:
Three-dimensional fitting software described in the present embodiment is the V-stitcher software of Gerber company of the U.S., and described Matlab is Matlab7.1 version.
A kind of method that improves virtual human body modeling accuracy in three-dimensional fitting software provided by the invention, comprises the steps:
The impact of the Human Modeling parameters on human pattern of body form change in the first step, confirmation three-dimensional fitting software, selected 11 affect the output parameter of larger build modeling parameters as BP neural network to body surface;
Second step, according to selected output parameter, analyze 36 physical characteristic parameters that may exert an influence to output parameter, as the input parameter of BP neural network;
The 3rd step, under Matlab environment, build the initial model of BP network, pass through network training, analyze minimax method, variance method for normalizing, and three kinds of methods of non-processor are carried out the impact on neural network forecast ability after standardization to data respectively, analyze the different network numbers of plies impact on neural network forecast ability is set, analyze the impact of different training function setup on neural network forecast ability, analyze different node transport functions the impact on neural network forecast ability is set, analyze different the number of hidden nodes the impact on neural network forecast ability is set, analyze heterogeneous networks input and output parameter the impact on neural network forecast ability is set, obtain optimum network structure, set up physical characteristic parameter and the shape adjustment nonlinearity in parameters mapping relations for three-dimensional fitting software,
The coded system of human parameters modeling interface document in the 4th step, analyzing three-dimensional fitting software, the conversion parameter obtaining according to the 3rd step and corresponding anthropometric data, generate the customization virtual human body based on client's build and human dimension.
Described 11 are preferably the larger build modeling parameters of body surface impact: shoulder parameter, chest locations parameter, chest peak distance parameter, abdomen parameter, health width parameter, buttocks width parameter, upper part of the body long parameter, pose parameter, lower part of the body pose parameter, bottom position parameter and upper body pose parameter.
36 described physical characteristic parameters are preferably: front fillet, rear fillet, average fillet, side neck is put the distance of chest point, side neck is put the distance/front length of chest point, BP dot spacing, BP dot spacing/chest measurement, abdomen coign, Attacking Midfielder's central point is to the angle of chest both sides, side waist central point is to the angle of chest both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of chest both sides to chest both sides, Attacking Midfielder is to the vertical angle of chest, Attacking Midfielder's central point is to the angle of buttocks both sides, side waist central point is to the angle of buttocks both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of buttocks both sides to buttocks both sides, Attacking Midfielder's central point is to the angle of angle/Attacking Midfielder central point chest both sides of buttocks both sides, the angle of side waist central point to angle/side waist central point of buttocks both sides to chest both sides, low back is to the vertical angle of stern, the back of the body is long, back of the body length/height, side knee central point is to the vertical angle of waist central point, side waist central point is to the angle of stern central point, lower body shaft angle, axon angle above the waist, angle, center before upper body, low back is to collare angle, highly, neck circumference, shoulder breadth, chest measurement, under chest, enclose, waistline, lower hip circumference, upper hip circumference, outer leg length and interior length.
The preferred minimax method of the 3rd step is processed data.
Optimum network structure described in the 3rd step is: the neural network number of plies adopts 4 layers, and the training function setup of network is that { trainlm}, the node transport function of network is set to { logsig, purelin, logsig}.
Described three-dimensional fitting software has build input parameter, shape adjustment parameter and human parameters modeling interface document, is preferably the V-stitcher software of Gerber company of the U.S..
Embodiment
The single shape adjustment parameter of virtual human body of controlling in three-dimensional fitting software changes, while observing this parameter variation, and the Changing Pattern of virtual human body build.The result of observing shows that the shape adjustment parameter in three-dimensional fitting software is used for virtual human body shoulder, chest, belly, buttocks, people's body side surface curve, chest waist, these 7 human bodies of stern waist to carry out build adjusting.And obtaining that the larger shape adjustment parameter of body surface impact is had to 11, these parameters are: shoulder parameter, chest locations parameter, chest peak distance parameter, abdomen parameter, health width parameter, buttocks width parameter, upper part of the body long parameter, pose parameter, lower part of the body pose parameter, bottom position parameter and upper body pose parameter.The major effect of the pattern of body form change of above-mentioned parameter to virtual human body is: the variation at shoulder parameter influence virtual human body shoulder slope angle of inclination, the variation of chest locations parameter influence virtual human body breastheight, chest peak separation is from the variation of parameter influence virtual human body BP dot spacing, the variation of the prominent degree of abdomen parameter influence virtual human body abdomen, health width parameter affects chest, waist, the variation of the radius vector ratio of buttocks, buttocks width parameter affects the variation of the radius vector ratio of buttocks, long parameter affects the variation of cervical vertebra point position at vertical direction above the waist, pose parameter affects the side curvilinear motion of health, lower part of the body posture affects the variation of waist to the shaft angle of knee, the variation at Bottom Position parameter influence lower part of the body axon angle, the upper body pose parameter impact variation at axon angle above the waist.
With reference to " an apparel modeling theory section " body surface measurement of angle position, human body side, human body thickness measure position, the impact of GBT16160-2008 " somatometric position and method for clothes " and shape adjustment parameters on human pattern of body form change, obtains 36 characteristics of human body's parameters: front fillet, rear fillet, average fillet, side neck is put the distance of chest point, side neck is put the distance/front length of chest point, BP dot spacing, BP dot spacing/chest measurement, abdomen coign, Attacking Midfielder's central point is to the angle of chest both sides, side waist central point is to the angle of chest both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of chest both sides to chest both sides, Attacking Midfielder is to the vertical angle of chest, Attacking Midfielder's central point is to the angle of buttocks both sides, side waist central point is to the angle of buttocks both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of buttocks both sides to buttocks both sides, Attacking Midfielder's central point is to the angle of angle/Attacking Midfielder central point chest both sides of buttocks both sides, the angle of side waist central point to angle/side waist central point of buttocks both sides to chest both sides, low back is to the vertical angle of stern, the back of the body is long, back of the body length/height, side knee central point is to the vertical angle of waist central point, side waist central point is to the angle of stern central point, lower body shaft angle, axon angle above the waist, angle, center before upper body, low back is to collare angle, highly, neck circumference, shoulder breadth, chest measurement, under chest, enclose, waistline, lower hip circumference, upper hip circumference, outer leg is long, interior length.
In Matlab7.1, build the initial model of BP network, pass through network training, to use respectively minimax method, variance method for normalizing and three kinds of method data after treatment of non-processor, in network, train, train respectively after 10 times, add up the square error of their 10 training, maximum error and the average AME of prediction.Result shows, use the data processed of method for normalizing fewer than former data time used in BP modeling is trained, and in method for normalizing, use the data that minimax method was processed to carry out BP training than the data that use variance method for normalizing to process, time used is few, and precision is high.Therefore select minimax method to carry out pre-treatment to data.
By the neural network of the heterogeneous networks number of plies, different training functions, different node transport functions combines network structure is arranged.By above network structure to training with the input parameter that minimax method was processed.Train respectively after 10 times, add up the square error of their 10 training, maximum error and the average AME of prediction.Result shows, the neural network number of plies adopts 4 layers, the training function setup of network is { trainlm}, the node transport function of network is set to { logsig, purelin, when logsig}, the maximum error of neural network forecast and average error minimum, the square error of network training within the acceptable range, obtains best network structure.
Using 11 idiotype parameters as independent output, build respectively 11 neural networks, use MIV(Mean Impact Value) 36 of network input parameters are screened, the input parameter respectively predicted value of 11 idiotypes adjustment parameters being had the greatest impact in Analysis deterrmination 36 idiotype characteristic parameters, sets up 4 layers of neural network of the single output of input more than 11.
The coded system of the interface document of human parameters modeling in analyzing three-dimensional fitting software, set up third party software platform, built-in neural network structure, by the physical characteristic data file input that 3-D scanning is obtained, through the parameter conversion of neural network, automatically generate and be applied to manikin interface document in three-dimensional fitting software.By the interface document generating, import in three-dimensional fitting software, can generate the three-dimensional virtual human body accordingly with customization physical characteristic.
Visible in sum: to the present invention is based on the basis of anthropometry, use BP neural network, set up the Nonlinear Mapping relation of build input parameter in three-dimensional virtual human body girth size, height dimension, length dimension, angle-data and three-dimensional fitting software, adopt characteristics of human body parameter to be converted to be applied to the shape adjustment parameter of three-dimensional fitting software to reach the method that improves virtual human body modeling accuracy.Can not only meet for the virtual fitting personalization of upper garment fit assessment, the Human Modeling demand of customization, and can provide technical support for the customization of clothes industry, the efficient generation from characteristics of human body's parameter to personalized human body can be realized, custom made clothing platform can be widely used in.
Finally be necessary to be pointed out that at this; above-mentioned explanation is only for being described in further detail technical scheme of the present invention; can not be interpreted as limiting the scope of the invention, some nonessential improvement that those skilled in the art's foregoing according to the present invention is made and adjustment all belong to protection scope of the present invention.
Claims (6)
1. a method that improves virtual human body modeling accuracy in three-dimensional fitting software, is characterized in that, comprises the steps:
The impact of the Human Modeling parameters on human pattern of body form change in the first step, confirmation three-dimensional fitting software, selected 11 affect the output parameter of larger build modeling parameters as BP neural network to body surface;
Second step, according to selected output parameter, analyze 36 physical characteristic parameters that may exert an influence to output parameter, as the input parameter of BP neural network;
The 3rd step, under Matlab environment, build the initial model of BP network, pass through network training, analyze minimax method, variance method for normalizing, and three kinds of methods of non-processor are carried out the impact on neural network forecast ability after standardization to data respectively, analyze the different network numbers of plies impact on neural network forecast ability is set, analyze the impact of different training function setup on neural network forecast ability, analyze different node transport functions the impact on neural network forecast ability is set, analyze different the number of hidden nodes the impact on neural network forecast ability is set, analyze heterogeneous networks input and output parameter the impact on neural network forecast ability is set, obtain optimum network structure, set up physical characteristic parameter and the shape adjustment nonlinearity in parameters mapping relations for three-dimensional fitting software,
The coded system of human parameters modeling interface document in the 4th step, analyzing three-dimensional fitting software, the conversion parameter obtaining according to the 3rd step and corresponding anthropometric data, generate the customization virtual human body based on client's build and human dimension.
2. the method for virtual human body modeling accuracy in raising three-dimensional fitting software according to claim 1, it is characterized in that, described 11 on the larger build modeling parameters of body surface impact are: shoulder parameter, chest locations parameter, chest peak distance parameter, abdomen parameter, health width parameter, buttocks width parameter, upper part of the body long parameter, pose parameter, lower part of the body pose parameter, bottom position parameter and upper body pose parameter.
3. the method for virtual human body modeling accuracy in raising three-dimensional fitting software according to claim 1, is characterized in that, 36 described physical characteristic parameters are: front fillet, rear fillet, average fillet, side neck is put the distance of chest point, side neck is put the distance/front length of chest point, BP dot spacing, BP dot spacing/chest measurement, abdomen coign, Attacking Midfielder's central point is to the angle of chest both sides, side waist central point is to the angle of chest both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of chest both sides to chest both sides, Attacking Midfielder is to the vertical angle of chest, Attacking Midfielder's central point is to the angle of buttocks both sides, side waist central point is to the angle of buttocks both sides, the angle of Attacking Midfielder's central point to angle/side waist central point of buttocks both sides to buttocks both sides, Attacking Midfielder's central point is to the angle of angle/Attacking Midfielder central point chest both sides of buttocks both sides, the angle of side waist central point to angle/side waist central point of buttocks both sides to chest both sides, low back is to the vertical angle of stern, the back of the body is long, back of the body length/height, side knee central point is to the vertical angle of waist central point, side waist central point is to the angle of stern central point, lower body shaft angle, axon angle above the waist, angle, center before upper body, low back is to collare angle, highly, neck circumference, shoulder breadth, chest measurement, under chest, enclose, waistline, lower hip circumference, upper hip circumference, outer leg length and interior length.
4. the method for virtual human body modeling accuracy in raising three-dimensional fitting software according to claim 1, is characterized in that: the 3rd step selects minimax method to process data.
5. the method for virtual human body modeling accuracy in raising three-dimensional fitting software according to claim 1, it is characterized in that, optimum network structure described in the 3rd step is: the neural network number of plies adopts 4 layers, the training function setup of network is { trainlm}, the node transport function of network is set to { logsig, purelin, logsig}.
6. according to the method for virtual human body modeling accuracy in the raising three-dimensional fitting software described in any one in claim 1 to 5, it is characterized in that: described three-dimensional fitting software has build input parameter, shape adjustment parameter and human parameters modeling interface document.
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| CN104881892A (en) * | 2015-06-04 | 2015-09-02 | 厦门唯尔酷信息技术有限公司 | Model deformation method and model deformation system |
| CN107154071A (en) * | 2016-03-02 | 2017-09-12 | 南京航空航天大学 | The method that Case-based Reasoning generates individual face body Model according to anthropological measuring size data |
| CN108197654A (en) * | 2018-01-03 | 2018-06-22 | 杭州贝嘟科技有限公司 | Stature data predication method, device, storage medium and equipment based on SVM algorithm |
| CN108876881A (en) * | 2018-06-04 | 2018-11-23 | 浙江大学 | Figure self-adaptation three-dimensional virtual human model construction method and animation system based on Kinect |
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| CN111742350A (en) * | 2018-02-21 | 2020-10-02 | 株式会社东芝 | Virtual fitting system, virtual fitting method, virtual fitting program, information processing device, and learning data |
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- 2012-12-20 CN CN201210559590.2A patent/CN103886117B/en not_active Expired - Fee Related
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| CN107154071A (en) * | 2016-03-02 | 2017-09-12 | 南京航空航天大学 | The method that Case-based Reasoning generates individual face body Model according to anthropological measuring size data |
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| CN108876881A (en) * | 2018-06-04 | 2018-11-23 | 浙江大学 | Figure self-adaptation three-dimensional virtual human model construction method and animation system based on Kinect |
| CN113474824A (en) * | 2019-02-25 | 2021-10-01 | 松下知识产权经营株式会社 | Evaluation system, spatial design support system, evaluation method, and program |
| CN110135078A (en) * | 2019-05-17 | 2019-08-16 | 上海凌笛数码科技有限公司 | A kind of human parameters automatic generation method based on machine learning |
| CN110135078B (en) * | 2019-05-17 | 2023-03-14 | 浙江凌迪数字科技有限公司 | Human body parameter automatic generation method based on machine learning |
| CN110705023A (en) * | 2019-08-30 | 2020-01-17 | 杭州海飘科技有限公司 | Somatosensory support technical method based on neural network algorithm |
| CN112200717B (en) * | 2020-10-26 | 2021-07-27 | 广州紫为云科技有限公司 | Complex garment virtual fitting method and device based on neural network and storage medium |
| CN112200717A (en) * | 2020-10-26 | 2021-01-08 | 广州紫为云科技有限公司 | Complex garment virtual fitting method and device based on neural network and storage medium |
| CN117541685A (en) * | 2023-11-08 | 2024-02-09 | 另一个我(北京)虚拟科技开发有限公司 | Virtual mapping human body model construction method and system for personalized service requirements and personalized service platform |
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