Converting lower-body features from three-dimensional body images into rules for individualized pant patterns

Abstract

This paper focuses on a new method to classify and convert the lower-body features of young females into the rules of individualized elements that are necessary for pant pattern design. Approaches used to develop a customized apparel pattern and their advantages and disadvantages are discussed in the literature review. Subsequently, a new method to analyze lower-body features for the development of individualized pant patterns is presented. The main method involved is the classification of certain body features into grades that provided alternatives for different body shapes, and the use of elements that are needed for pattern-making but that are difficult to measure directly in a three-dimensional image to predict the girths, half-girths and the other key measurements of a body. Three sets of rules were derived from this method: (a) individualized rules on height to classify the height at a feature into different grades, which help to find the location of the feature based on the height of a body; (b) individualized rules on girth to sort the thickness/width ratio of different cross-sections into grades at different features, which predict the girth of a characteristic feature from the thickness and the width of a feature; and (c) individualized rules on the crotch to find the relationship between the length of the measuring line at the divided points and the thickness of a certain girth, which reveal the individuality of the crotch and benefit the development of an individualized pant pattern. The method presented has the potential to realize individuality in female pant design by converting lower-body features into grades and establishing relationships with personalized parameters. This method can reduce the complexity of customization in pattern design.

Keywords

body features apparel customization pattern-making 3D body scanning

In today's apparel market, consumers wish to purchase high-quality, well-fitting clothing with at a fair price. With the advent of digital technology and E-commerce, the costs of materials and transactions to make clothing have been reduced greatly. The demand for customized products has also risen significantly.^1–2 One promising trend in the field of apparel manufacturing is the need for manufacturers to find a way to meet this demand and change from mass production of clothing to made-to-measure or, more advanced, mass customization.

For the past decade, new technologies—such as three-dimensional (3D) body scanning for anthropometry, big data for analysis of body types, intelligent pattern-making for quick-response systems, flexible manufacturing for sewing systems and virtual try-on technology for fitting—have greatly pushed forward mass-customization operations for apparel. Among them, the technology to make personalized patterns remains the core of customization.

Different researchers have developed different approaches for the generation of individualized patterns from body data. As summarized by Shan et al.,³ these approaches may be divided into two categories. The first category includes methods of pattern modeling and flattening by computers. It is logical to expect that a good-fit pattern can be obtained by flattening the 3D model of a person's scanned body. This approach requires a person to be scanned, a 3D model with a smooth surface to be reconstructed from the scanned data and flattening of this surface into garment pattern pieces using an appropriate algorithm. In the development of a 3D garment Computer Aided Design (CAD) system, considerable attention has to be paid to the flattening algorithms of a 3D surface.^4–7 Given a 3D free-form surface and material properties, the corresponding flat patterns could be generated through a flattening process. The main advantage of this approach is that it generates pattern pieces directly from a 3D virtual model and translates the shape features of a customer's figure into the pattern. But it is hard to integrate this virtual design approach into the current pattern-making system. This approach seems to lack the practicability and accuracy needed by the industry.⁷

The second category includes methods to alter a ready-to-wear pattern to a personalized pattern using experience-based rules.^8–12 Artificial intelligence (AI), especially artificial neural networks (ANNs) and fuzzy logic (FL), have been proven to be feasible and useful in developing models for nonlinear systems. From the view of ANN, apparel patterns are a set of elements including points, lines, curves and sizing, and pattern-making is a process to configure the correlation of these factors. FL can deal with vague, ambiguous or imprecise problems, such as fit or ease of a pattern in clothing science, to generate a definite conclusion. The utility of both ANN and FL requires parameters being found that describe the topological relationships between points, lines and curves in pattern-making. However, most of the AI methods are only used as a tool to facilitate the process of pattern-making, which still heavily depends on an operator's experience.³

As shown in Figure 1, the female's lower-body surface can be seen as the tightest clothing that covers the body without any ease.¹³ This view settles the reflection between characteristic features and elements of a pattern. A personalized pant in this paper is defined as a pant that fits well with a wearer. The “fit” means that curves in a pattern match the characteristic features of a body, such as the waist, abdomen, hip and thigh. Fitted clothing can provide a wearer with a comfortable feeling and appropriate ease. Clothing fit relates to garment style, fabric properties, the pattern-making method, sewing and ease allowance.^14,15 The influence of these factors and a method to quantize them for the fit of pants has been explored in two earlier papers.^14,16 In order to push forward our research project, some of this earlier work has been integrated and reproduced in this paper. The relevant figures (Figures 2 to 4) and tables (Tables 5 to 8) are explicitly referenced where the figures or the tables are discussed. This paper mainly focuses on the relationship between lower-body features and the shape of curves required to develop a pant pattern.

Figure 1.

Relationship between features of lower body and curves in pant pattern. A: abdomen, B: buttock, FW: front waist, BW: back waist, SW: side waist, FH: front hip, BH: back hip, CR:crotch, SH: side hip, SF: side femur, BF: back femur.

Figure 2.

Method to measure the height, width and thickness of a body.

Figure 3.

Method to measure front and back features.

Figure 4.

Method to measure the crotch.

Pattern-making for clothing with a good fit requires the characteristic features of a body to be known, and the body features and curve parameters in a customized pattern to be mutually adaptive.^11,13–16 The level of individualization of the pattern depends on the fitting level of curves with their corresponding features. Originating from this concept, our study intends to develop a method that combines the advantages of the two pattern-alteration categories discussed above for the development of personalized patterns. This method begins with the relationship between a body and an apparel pattern in order to convert information about a body's features into the shape of an element in a pattern, and then classifies body features into grades to express individual characteristics.

Experiments

A total of 416 female participants who came from East China—mainly Jiangsu province, Shandong province, Henan province and Anhui province—were scanned using a SYMCAD 3D whole-body scanner, made by TELMAT INDUSTRIE, France. The participants were aged between 21 and 24 years, had never been pregnant and their body mass indexes were limited to categories between 19 and 26. Participants were scanned in standing posture A according to International Organization for Standardization (ISO) 20685-2010.¹⁷

For the purpose of getting 3D information about the body surface, this experiment did not use the built-in software of the body scanner. All 3D data were taken out of the scanner and visualized by different software, Imageware, which is widely used in the automobile, aviation, household electrical appliances, components of personal computer fields etc. to design and make A-class surfaces due to its powerful ability to process and edit point-clouds data. Imageware was used in this paper to read, visualize, reconstruct and measure the data clouds of scanned bodies.¹⁸ Statistical software, SPSS, was adopted for data analysis. In this study, body features including the waist, abdomen, hip, thigh, knee and ankle were selected as the main factors that influence the shape of a pant.

According to principle of body pattern of Masaru and Yuan,¹³ a body's surface can be taken as the tightest clothing and thus the locations of body features as the frame of a pattern. Moreover, according to the inherent relationship between a body and clothing, the shape of a body's feature influences the shape of a curve in the pattern, and individual features lead to individualized curves and subsequently an individualized pattern. Therefore, the analysis of a body's features becomes an essential element to realize individuality in an apparel pattern. In this paper, the individualization was divided into three parts: height, girth, and the crotch; individualized rules were studied for each part.

The methods used to measure the features of a body—such as the waist, abdomen, hip, thigh, knee and ankle—were carried out according to ASTM D 5219-2015.¹⁹ However, it is inadequate to describe the uniqueness of a body simply by using these features. Hence, a few self-defined measurements were introduced. The concept to reflect the individuality of a body used in this paper was previously mentioned, but not fully explained (as in Figure 2), by Su et al.¹⁶ Our paper provides a complete illustration of the lower-body features in 3D body images (see Figures 2 to 4).

In the process of measuring, a body's features in 3D are described as height, width and thickness in its front and side views. The width of a certain feature is the width in the front view of the feature's position and the projected length if the girth is inclined. Moreover, in the side view, we defined the lateral line to be a line that links the midpoint of the thickness of the waist and the midpoint of the thickness of the calf. Based on the lateral line, each feature is divided into front and back parts. See Figure 2 for more detail.

The inner leg line was identified by viewing each digital twin of each participant and linking the crotch point to the midpoint of thickness of the knee at the side of the inner leg. The landmark of the crotch point was located according to ISO 8559-1:2017.²⁰ Thus, the lower-body surface, which can also be looked on as a pant, was divided into two parts. More significantly, the body features and curves in a pant pattern were related. Therefore, the fronts and backs features were measured using Imageware one-by-one, as shown in Figure 3.

The shape of the crotch plays an important role in the structure of pant, which concerns the shape and the comfort of wearing of a pant. It is hard to measure a crotch on a real body by traditional methods, but with the help of 3D scanning data, a digital twin of a buttock or crotch was extracted from the body scan data using a function of Imageware, as shown Figures 4 and 7.

For the sake of accuracy, the whole crotch was split into two parts (the front crotch and the back crotch) at the point of the crotch as shown in Figure 4. The lengths of the front and back crotch, the central front line (C. F. line) and the central back line (C. B. line) were measured.

Individualized rules on height

The location of a feature with regard to height reflects characteristic of a body and influences the shape of a curve in the structure of a pattern. Regarding future intelligent manufacture in the field of customization, it is essential for the rules to judge the location of a feature in the height direction of a body, so the relationship between the height of a feature and the height of a body must be explored. The ratio of height of features and body height was introduced as $R H_{feature} = \frac{heightoffeature}{bodyheight}$ , in which the subscript indicates the relevant feature's name.

The height of each feature was measured using Imageware and the ratio was calculated. Next, these ratios were sorted from A to Z and classified by 0.1 at each grade to count the quantity that the ratio belongs to in a certain interval. The results are shown in Table 1.

Table 1.

Distribution of features at height

	0.15–0.24	0.25–0.34	0.35–0.44	0.45–0.54	0.55–0.64	0.65–0.75	Total
$R H_{waist}$				6	406	4	416
$R H_{abdomen}$				345	71		416
$R H_{hip}$		3	200	213			416
$R H_{thigh}$	1	59	328	28			416
$R H_{Knee}$	149	267					416

RH: Ratio of height.

Table 1 shows a clear and high concentration of the ratio at each feature. $R H_{waist}$ ranges from 0.55–0.64, which indicates that > 97% of participants were in accordance with this rule. $R H_{abdomen}$ and $R H_{thigh}$ show almost the same ratios with different quantities. For $R H_{hip}$ and $R H_{Knee}$ , there are two centers for concentration, which indicates that there are two kinds of shape for the two features.

In the process of data analysis, correlation analysis between the height of each feature and the height of the body was made. Table 2 shows the results of the correlation analysis between the two factors. It shows that all of the R² values (at a level of 0.01) were relatively high, and all significances (two-tailed) were 0.000 < 0.01, which indicates that the two variables have significant correlation. Thus, it is feasible to get the relationship between the height of features and the height of a body using a method of linear regression.

Table 2.

Correlation analysis between each feature and body at height

	R ²	N
Waist height and body height	0.866**	416
Abdomen height and body height	0.821**	416
Hip height and body height	0.836**	416
Thigh height and body height	0.874**	416
Knee height and body height	0.794**	416

indicates P < 0.01.

Scatter diagrams showing the height of each feature and the height of a body were constructed to observe the trends of distribution (e.g. for waist in Figure 5). Based on the scatter diagrams and the correlation analyses, a linear regression was built up as shown in equation (1), where H is the height of a feature, a is a constant, b is the regression coefficient and x is body height. The constants and regression coefficients were obtained by regression analysis and are summarized in Table 3.

H = a + b * x

(1)

Figure 5.

Scatter diagram between waist height and body height.

Table 3.

Constants and regression coefficients at features

	a	b
Waist	−8.736	0.683
Abdomen	−9.215	0.660
Hip	−9.847	0.558
Thigh	−11.726	0.498
Knee	−7.968	0.353

To test the accuracy of regression, an error check was carried out by comparing the valued predicted by the regression with the one measured by the body scanner. We arranged the absolute error with a 1 cm grade and counted the quantities that fell into a certain interval; the results are shown in Table 4. It indicates that > 50% of data have absolute errors within 1 cm, and ∼ 80% within 2 cm, which is acceptable for pattern-making.²¹

Table 4.

Error check for regressions at height

		0–1 cm	1–2 cm	2–3 cm	> 3 cm
Waist	Quantity	221	101	63	31
	Proportion	53.1%	24.3%	15.1%	7.5%
Abdomen	Quantity	219	98	70	29
	Proportion	52.6%	23.6%	16.8%	7.0%
Hip	Quantity	231	99	61	25
	Proportion	55.5%	23.8%	14.7%	6.0%
Thigh	Quantity	208	84	61	63
	Proportion	50.0%	20.2%	14.7%	15.1%
Knee	Quantity	284	90	34	8
	Proportion	68.3%	21.6%	8.2%	1.9%

Table 5.

Distribution of ratio for girths

Grade	1	2	3	4	5	6
Grade value	0.51–0.55	0.56–0.60	0.61–0.65	0.66–0.70	0.71–0.75	0.76–0.81
Waist	8	60	112	146	78	12
Abdomen	6	71	120	132	75	11
Hip	12	105	167	102	27	3
Thigh	23	126	132	85	31	19
Knee	45	188	100	62	16	5
Calf	12	86	152	115	42	9

Individualized rules on girth

The shape of a cross-section at a characteristic position gives the most exact information on body figure and determines the lengths and shapes of curves in the structure of an apparel pattern. The characteristic cross-sections usually relate to, but are not the key measurements for, the pattern design. We intended to develop a system that could predict the girth of a characteristic feature from the thickness and width of a feature, which easy to measure from a common digital picture. Thus, the ratio of thickness and width was adopted to reflect a feature's individuality as $R G_{feature} = thickness of feature width of feature$ , in which the subscript indicates the relevant feature's name, and the thicknesses and widths features were measured from the front and side views from the corresponding 3D scan data. It should be noted that the general rules on girth developed in our previous work¹⁶ are cited in Tables 5 to 8 in this paper with better illustration and more explanation.

The thicknesses and the widths of cross-sections at each feature were measured using Imageware, and the ratio was calculated. A box plot in SPSS was used to eliminate any abnormal values. Subsequently, the maximum and minimum the ratio were found and classified with a 0.05 difference. Further, the quantity that fell into a certain grade was counted and the results are shown in Table 5.

It can be seen from Table 5 that the ratio mainly concentrates on one or two grade(s) for each feature. It was therefore logical to explore the exact relationship between the width, the thickness and the girth, and conduct a correlation analysis of the width and thickness of feature and the girth of a cross-section. Table 6 shows the results; all of the R² values are relatively high and significances (two-tailed) are 0.000 < 0.01, which illustrates that the variables have significant correlation. Thus, it is feasible to get the relationship between the width and thickness of a feature and its girth by a method of regression.

Table 6.

Correlation analysis between the width, thickness and girth of features

	Width of waist		Thickness of front waist
	R ²	Significance (t-tailed)	R ²	Significance (two-tailed)
Front waist	0.898*	0.000	0.865*	0.000
Front abdomen	0.886*	0.000	0.849*	0.000
Front hip	0.822*	0.000	0.806*	0.000

	Width of waist		Thickness of back waist
	R ²	Significance (two-tailed)	R ²	Significance (two-tailed)
Back waist	0.856*	0.000	0.917*	0.000
Back abdomen	0.801*	0.000	0.883*	0.000
Back hip	0.787*	0.000	0.836*	0.000

	Width of thigh		Thickness of thigh
	R ²	Significance (two-tailed)	R ²	Significance (two-tailed)
Thigh	0.790*	0.000	0.793*	0.000
Knee	0.766*	0.000	0.720*	0.000
Calf	0.715*	0.000	0.769*	0.000

indicates P < 0.01.

Scatter diagrams showing the width and thickness of each feature and their girth were made to observe trends of distribution. Two examples are included for explanation, the front waist and thickness of the front waist in Figure 6 (a) and the front waist and width of waist in Figure 6 (b).

Figure 6.

Scatter diagrams showing front waist and thickness (a)/width (b) of the front waist (in grade 4).

Figure 7.

Method to measure a crotch. C. F. Line: crotch front line, C. B. Line: crotch back line.

Based on the scatter diagrams and the correlation analyses, linear regressions were built up as shown in equations (2) and (3), where FW and BW are front and back waist, respectively; $a_{0}$ and $b_{0}$ are constants, $a_{1}$ , $a_{2}$ , $b_{1} and b_{2}$ are regression coefficients; $T_{fw}$ is the thickness of the front waist; $T_{bw}$ is the thickness of the back waist; and W is the waist.

FW = a_{0} + a_{1} * T_{fw} + a_{2} * W

(2)

BW = b_{0} + b_{1} * T_{bw} + b_{2} * W

(3)

The constants and the regression coefficients were obtained by regression analysis at each characteristic position. Because of the similar regression procedure, only one example of the waist is included as an illustration (see Table 7).

Table 7.

Constants and coefficients at the waist

Grade	$a_{0}$	$a_{1}$	$a_{2}$	$b_{0}$	$b_{1}$	$b_{2}$
1	−32.350	1.407	0.927	−96.854	2.064	1.084
2	−3.024	1.285	0.842	−42.287	−0.078	1.457
3	8.468	1.483	0.743	−52.825	1.816	−0.991
4	9.988	0.606	1.023	−51.847	0.632	1.375
5	−8.337	1.792	0.680	−42.454	1.147	1.157
6	1.549	−0.261	1.439	−51.653	2.327	0.786

Table 8.

Error check for regressions on girths

		0–1 cm	1–2 cm	2–3 cm	> 3 cm
Front waist	Quantity	307	91	16	2
	Proportion	73.8%	21.9%	3.8%	0.5%
Front abdomen	Quantity	375	40	1	0
	Proportion	90.1%	9.6%	0.2%	0.0%
Front hip	Quantity	325	80	9	2
	Proportion	78.1%	19.2%	2.2%	0.5%
Back waist	Quantity	328	80	7	1
	Proportion	78.8%	19.2%	1.7%	0.2%
Back abdomen	Quantity	369	46	1	0
	Proportion	88.7%	11.1%	0.2%	0.0%
Back hip	Quantity	347	61	5	3
	Proportion	83.4%	14.7%	1.2%	0.7%
Thigh	Quantity	185	141	61	29
	Proportion	44.5%	33.9%	14.7%	7.0%
Knee	Quantity	228	122	38	28
	Proportion	54.8%	29.3%	9.1%	6.7%
Calf	Quantity	300	92	23	1
	Proportion	72.1%	22.1%	5.5%	0.2%

Also, an error check for regression at each feature was carried out. We arranged the absolute error with 1 cm grades and counted the number that fell into a certain grade; the results are shown in Table 8. They shows a good and high concentration for the front and back features, and most participants have an absolute error within 1 cm, which is reliable for body measurement and further analysis.

Individualized rules on crotch

A well-fitting crotch plays a vital role in the good appearance and comfort of a pant. However, the curve of a crotch in traditional methods of pattern-making is usually inferred from other elements, e.g. the hip, which somewhat lacks individualized characteristics. We supposed that the shape of a crotch is influenced by its width and depth. In other words, that these two parameters can be used to describe differences in crotch shapes. In order to reveal the individuality of the crotch, the regression between the width and the depth of a crotch and the corresponding crotch length should be explored. We used the scan data to learn the exact shape of crotch by the following method.

Firstly, a longisection was generated by taking a cross-section across the crotch point using function of the point-cloud intersection in Imageware.¹⁷ Meanwhile, the positions of the waist, abdomen and hip were determined; thus the shape of a crotch was clear. It is important to note that the location of the waist in a virtual environment is hard to determine. ISO 20685:2010¹⁷ and ISO 8559-1 2017²⁰ were used to help decide the position of the waist in this paper.

Next, a frame for measuring was set up based on the longisection. A cross-section that is perpendicular to axis Z and across the crotch point was made, which divided the whole crotch into two parts: the front and the back. The front part was further divided into two segments by the ventral convex point, so the C. F. line and the front crotch were developed. In the back part, the gluteal point was used to develop the C. B. line and back crotch. A horizontal line across the ventral convex point intersects at point A with another vertical line across the crotch point at front part, so did in the back part and point B. Furthermore, the distance $\bar{O O_{1}}$ was equally divided into five parts, and then lines vertical to $\bar{O O_{1}}$ were drawn through equidistant points and intersected with the front crotch line at points A_1–4. A similar procedure for the back crotch and got points B_1–4.

Finally, the lengths of D_1–3, La_1–4 and Lb_1–4 were measured. Figure 7 illustrates this process of measuring.

As indicated above, the trends and correlations among data on every part of the crotch were listed and verified. The regressions and the results of error checks for the lengths of every part of the crotch are listed in Table 9.

Table 9.

Regression and error check on crotch

Element	Regression	Note	0–1 cm	1–2 cm
C. F. line	$CFL = - 0.679 + 0.613 * D_{3}$	$D_{3}$ : see Figure 7	65%	86%
C. B. line	$CBL = - 2.286 + 0.863 * D_{3}$	$D_{3}$ : see Figure 7	62%	94%
Front crotch	$C R_{f} = 25.631 + 0.063 * G_{fab} - 0.428 * D_{1}$	$G_{fab}$ : front abdomen $D_{1}$ : see Figure 7	60%	82%
L_a1	$L_{a 1} = - 0.011 + 0.954 * T_{fab}$	$T_{fab}$ : thickness of front abdomen	95%	100%
L_a2	$L_{a 2} = 0.167 + 0.858 * T_{fab}$	$T_{fab}$ : thickness of front abdomen	96%	100%
L_a3	$L_{a 3} = 0.954 + 0.671 * T_{fab}$	$T_{fab}$ : thickness of front abdomen	91%	100%
L_a4	$L_{a 4} = 1.209 + 0.464 * T_{fab}$	$T_{fab}$ : thickness of front abdomen	89%	100%
Back crotch	$C R_{B} = 18.941 + 0.159 * G_{bab} - 0.056 * D_{2}$	$G_{bab}$ : back abdomen $D_{2}$ : see Figure 7	62%	84%
L_b1	$L_{b 1} = 0.217 + 0.972 * T_{bh}$	$T_{bh}$ : thickness of back hip	96%	100%
L_b2	$L_{b 2} = - 0.182 + 0.967 * T_{bh}$	$T_{bh}$ : thickness of back hip	96%	100%
L_b3	$L_{b 3} = - 0.302 + 0.910 * T_{bh}$	$T_{bh}$ : thickness of back hip	94%	100%
L_b4	$L_{b 4} = - 0.206 + 0.791 * T_{bh}$	$T_{bh}$ : thickness of back hip	87%	100%

C.B. line: central back line; C.F. line: central front line.

Summary

This paper presents a method to convert a body's lower features into individualized pant patterns to realize the individuality of a customer based on 3D body scanning data.

The main idea of behind this method was the classification of body features into grades that provide alternatives for different body shapes. The method involves three sets of rules: (a) individualized rules on height to classify the height at a feature into different grades, which help to find the location of the features at height based on the height of body; (b) individualized rules on girth to sort the thickness/width ratios of the different cross-sections into grades at different features, supporting the research on predicting the girth of a characteristic feature from the thickness and width of a feature, which are easy to measure from a common digital picture; and (c) individualized rules on the crotch to find the relationship between the length of the measuring line at the divided points and the thickness of a certain girth, which reveals the individuality of the crotch and benefits the development of an individualized pant pattern in the future.

The novelty of this method lies in the conversion of certain body features into grades that provide alternatives for different body shapes, and the use of elements to predict the girths, half-girths and other key measurements of the body, which are necessary for pattern-making but are difficult to measure in a 3D environment. This method has the potential to generate an individualized pant pattern from 3D body scanning data in a concise way.

Footnotes

Acknowledgements

The authors would like to thank the Natural Science Foundation of Jiangsu Province (Grant Number BK20151191), the National Natural Science Foundation of China (Grant Number 61702461) and the Fundamental Research Funds for the Central Universities (Grant Number JUSRP51735B) for financial support. This study was also supported by Jiangsu Intangible Cultural Heritage Research Base.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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21.

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