The current three-dimensional human models used in the apparel industry are mostly rigid and lack semantic information on body positions and body parts. Therefore, it is difficult for designers to make accurate, fast and effective designs from these models. This paper proposes a new parametric three-dimensional human body model based on key position labeling and optimized body parts segmentation. First, by using experts’ professional knowledge, we manually realize accurate human body data measurements as well as their interpretation and classification, and extract relevant human body features. After deep analysis, measured data irrelevant to body shape have been excluded by designers. Furthermore, the relation between body shapes and body features have been modeled. Second, based on this relational model, we label key positions on the corresponding three-dimensional body model obtained by scanning and segmenting the whole three-dimensional human body into semantically interpretable body parts. In this way, two databases have been created, enabling us to identify features of all segmented body parts, whose combination corresponds to the whole body shape. Third, for a specific consumer, his/her personalized three-dimensional human model can be obtained by taking a very few number of body measurements on himself/herself, making an appropriate combination of the identified body parts, and adjusting parameters of all involved body parts. By comparing the proposed labeled and segmented three-dimensional human model and the existing human models through a number of experiments, the proposed model leads to more relevant results with high accuracy and high visual quality related to real human body shapes.
Human body shape feature points are the key information and basic unit for human body model we are constructing, which performance the difference of body shapes. The purpose of the study is to extract the structural feature factors related to the upper body surface feature points for young females. The 12 feature points of upper body surface were manually confirmed from anthropometric expertise. A total of 31 measurements items, including 3 body surface measurement and 28 photo measurement, were collected for 33 females college students According to the results of correlation analysis, the feature variables of the width, thickness and height dimension based on 12 feature points significantly respectively correlated to the variables of their coordinate orientation, furthermore, the correlated relationship which reflected the width and height features of neck and shoulders shape mainly affected by local skeletal structures. Then, four principle component factors account for upper body shapes of young females, such as width, thickness, height and shoulder shape with characteristic value all over 1, were extracted by the principal component analysis, and the cumulative contribution rate reached 87.387%. Therefore, a total of 8 feature variables sifted from each principle component factor with a loading coefficient over 0.7 as fundamental typical indicators represent the three-dimensional characteristics of body surface feature points reflecting the divergence of body shapes, and it is useful structural information for individual human body modelling.
This paper is aiming to obtain an arm-root curve function performing the human arm-root size and shape realistically. A gypsum replica of upper arm for young male was made and scanned for extracting the 3D coordinates of 4 feature points of shoulder point, the anterior/posterior armpit point and the axillary point describing the real arm-root shape under the normalized definitions, and the 5 landmarks were confirmed additionally for improving the fitting precision. Then, the wholly and piecewise fitting of arm-root curve with 9 feature points and mark points in total were generated respectively based on least square polynomial fitting method. Comparing to the wholly fitting, the piecewise fitted function segmented by the line between anterior and posterior axillary points showed a high fitting degree of arm-root morphology with R-square of 1, the length difference between fitted curve and gypsum curve is 0.003 cm within error range. And it provided a basic curve model with standard feature points to simulate arm-root morphology realistically by curve fitting for accurate body measurement extraction.
This paper is proposed to extract the morphological factors of upper limb shapes related to the personalized sleeve design. The 36 items of lateral upper limb morphology were measured for 50 young female aged 19 to 22 years old by the photo measurement method. Based on the correlation relationships of morphological variables of upper limb, there are 4 morphological factors of upper limb shapes with eigenvalues above 1 were extracted by the principal component analysis, and the cumulative variance reached 83.504%. Among them, the factors of girth and height of upper limb were relevance to the girths of torso and stature respectively, and the factor of upper limb oblique angle is influenced by the upper body axis inclination, and the arm root height is interpreted as an independent factor describing the arm root shape. These morphological factors provided the references in key feature indicator sifting for upper limb shape subdivisions and the critical parameters in personalized sleeve structure designing.
To improve the fitness of bottoms of individuals, this study is proposed to extract lumbar shape factors influencing the ratio of waist dart’s volumes of young females. A total of 27 measurement items including lumbar shapes and waist dart’s volumes were collected through overlapped graphics of lumbar feature sections obtained from 30 female college students aged 18 and 24via 3D body scanning method. Results of correlation analysis showed that the total waist dart’s volume was significantly correlated with the lateral lumbar bending level (ytc), and the ratio of each waist dart’s volume was significantly correlated with lumbar anteversion angle (β), lumbar anteverted amount (ytq) and buttock projected volume (ab) respectively with a correlation coefficient above r=0.7. Furthermore, a total of 2 factors with a characteristic value that was larger than 1 were extracted through principle component analysis, whose cumulative contribution rate reached 80.241%. Principle Component 1 represented factors of lumbar anteversion, which was related to the ratio of anterior/posterior waist dart’s volume; and Principle Component 2 represented the lateral lumbar shape characteristics affecting the total waist dart’s volume.
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