基于物理引擎PhysX的3D试衣系统的设计与实现

为了实现3D试衣功能及解决虚拟试衣建模算法时间复杂度比较大的问题,该文结合虚拟现实等技术,首先用弹簧-质点模型对服装布料进行建模,然后以牛顿第二定律为理论基础分析以质点-弹簧模型建模的服装布料的受力情况,结合以GPU为基础的物理引擎PhysX,建立3D试衣间,仿真服装布料与人体模型的运动交互状况。最后加入人体模型调整、选衣、试衣等功能。该系统在Windows平台下,利用VisualStudio2008设计开发,结合虚拟现实等技术的综合应用,建立了一套包括选衣、试衣、人体参数调整等功能的3D试衣系统。     

服装CAD中个性化三维人体建模

针对三维服装仿真中对各种不同体态特征的人体模型的需求,给出了一种个性化三维人体建模方法。首先对一系列具有不同特征尺寸的成年女性的人体扫描数据进行简化处理,建立具有一致拓扑的人体模型;然后根据不同人体的对应数据点及其相应的特征尺寸,生成各个简化数据点随特征尺寸变化的规律。利用此变化特性,对参考人体模型进行变形得到新尺寸下的人体模型。该方法已在所开发的三维虚拟试衣系统实现,并取得了较为理想的试验效果。     

基于Unity3D的计算机虚拟试衣测量系统设计与实现

在Unity3D引擎环境下,针对虚拟试衣系统中由建模法生成的人体模型难以实现大量个性化定制的问题,提出了一种使用BlendShape算法调整人体各局部特征尺寸,并通过人体特征轮廓曲线约束调整过程的解决方案。该方案由一个基础人体模型向一组极限模型进行过渡变形得到大量个性化人体。为了实现动态试衣,研究了服装基于弹簧-质点模型的布料仿真与碰撞处理过程,由Verlet积分求解的特殊性,通过一组球形碰撞器实现了不同体型的人体模型试穿相同的服装,其结果相较于静态着装避免了穿透现象并提升了真实感。     

人体模型自适应变形算法的研究与实现

针对虚拟试衣个性化调节过程中服装尺寸符合人体模型时出现的人体模型表面穿透服装的失真现象,提出一种人体模型自适应变形算法,通过在需要自适应变形的区域加入质点弹簧系统,并在该系统中加入了一类新的弹簧——支撑弹簧,使得模型表面更接近于真实人体的情况,并对实现算法进行了研究和分析,针对存在的不足进行了改进。实验结果表明,该算法运算效率较高、响应时间短,使用后人体模型能够针对服装的形状进行自适应变形,并具有围度不变性,实现了较为理想的试衣效果。     

基于共形几何代数的三维人体骨架运动序列

为了解决虚拟试衣中人体模型可以动态展示服装效果的问题,提出三维人体骨架动态建模。在基于三维人体模型的骨架自动提取的基础上,采用共形几何代数方法描述人体关节点的运动并通过相邻关节点的位置变化表达不同的人体动作。用关键帧插值法实现人体姿态的序列动作,选择螺旋运动算子实现了基于2D视频图像的3D人体骨架姿态的参数估计,建立了骨架运动模型。三维人体骨架模拟视频动作证实了该方法的高效性。     

支持人体模型驱动的三维服装参数化设计*

通过驱动人体模型来改变服装模型的方法实现服装三维设计。首先利用基于特征线造型的方法构造人体模型和服装模型,再将服装模型特征框架（garment model feature frame, GMFF）相对人体模型特征框架(body model feature frame, BMFF)进行曲线编码,然后再利用参数化方法驱动BMFF变形后,再对GMFF相对BMFF进行曲线解码,从而得到驱动以后的GMFF。通过曲面插值,利用BMFF和GMFF得到更新以后的人体模型和服装模型。给出的实例表明,利用该方法可以非常快速有     

三维服装设计虚拟技术在电子商务中的应用研究

介绍了我国服装CAD技术与电子商务的发展与现状。针对电子商务营销中存在的诚信、动态实物展示缺失,不能满足网民的个性化和专业化的需求等问题,提出了构建新一代电子商务虚拟展示平台结构框架的设想。着重阐述了服装电子商务中三维虚拟关键技术,包括人体模型生成、在线试衣、动态展示等。     

网上3D试衣系统技术研究

虚拟试衣系统是一种应用于服装电子商务的实时交互平台。为了满足虚拟试衣系统真实性、实时性、方便性等设计要求,该文采用服装操控技术为虚拟服装建立三维模型,并把其中的交互建模标记绑定到服装模型上,以此取代手工指定的标记,从而把原来需要人机交互的三维服装建模过程转化成一个后台计算过程。保留服装操控技术中的表面拖曳交互机制,作为用户对建模完成之后的虚拟服装的微调手段,并把原来需要人机交互指定的拖曳不动点信息固化到服装模型数据文件,以此提高虚拟试衣间实际应用中的方便性。在网上3D试衣系统的设计中,提出了布片无缝拼接算法以及二面角调整算法,并在实验中取得了较好的效果。     

一种基于人体测量学的三维人体快速建模方法研究与实现

常用的个性化三维人体建模方法存在数据量大,处理过程复杂,不适合Web应用的不足,该文提出一种新的快速进行三维人体建模的方法,该方法根据人体测量学和人体解剖学原理,结合人体表面特征点和特征线分布规律,应用超椭圆仿真人体各截面、采用层次参数驱动设计思想,实现快速创建个性化三维人体模型。该方法具有满足实时要求、交互参数少,易于控制,误差小、模型真实感强等优点,并可以准确地提供骨骼关节点和人体特征点,适于三维仿真形象动态设计、人体运动模拟和虚拟服装试衣等多种应用。     

一种基于人体测量学的三维人体快速建模方法研究与实现

常用的个性化三维人体建模方法存在数据量大,处理过程复杂,不适合Web应用的不足,该文提出一种新的快速进行三维人体建模的方法,该方法根据人体测量学和人体解剖学原理,结合人体表面特征点和特征线分布规律,应用超椭圆仿真人体各截面、采用层次参数驱动设计思想,实现快速创建个性化三维人体模型。该方法具有满足实时要求、交互参数少,易于控制,误差小、模型真实感强等优点,并可以准确地提供骨骼关节点和人体特征点．适于三维仿真形象动态设计、人体运动模拟和虚拟服装试衣等多种应用。     

Automatic pose-independent 3D garment fitting

Given a virtual garment model on a reference human model, we propose an automated 3D garment fitting system that fits the garment model to a target human model. The proposed method can transfer garment models between human models without any user guidance even when the reference and target human models have different poses. Our goal is not to resize or deform the original garment model according to the target human model but to yield realistic fitting results of the given garment on the target human models. Using pose-independent segmentation and cloth simulation, we achieve realistic and automatic fitting results in reasonable running time. Our method can replace the time-consuming manual fitting process that is necessary for many applications that use virtual garments, such as games, animations, CAD tools and online clothing stores.     

An implementation of a garment-fitting simulation system using laser scanned 3D body data

We propose a garment-fitting system for an online retail model, which uses a consumer's 3D body data for garment fitting. This system uses front and back images to model the garment and 3D laser scanned body data to model the body. In order to recognize characteristic points on the 3D body data, a moment template composed of central moments of vector angle sets on 3D body data is proposed, and an implementation combining the 3D data and garment images is reported, which includes modeling, collision detection and feedback handling. The resulting system would support an online retail business model for garments. In the proposed business model, a body data center would scan the body and store the 3D data, the simulated garment-fitting program would be provided to individual consumers for fitting on a home PC, and retail shops would exhibit garment images on their websites. In this proposed model, security of personal data and computational cost will not be an issue.     

The development of an intelligent system for customized clothing making

This study presents the development of an intelligent system for customized clothing making. The system involves body dimension collection, clothing pattern generation and fabric cutting. First, body dimensions can be collected by analyzing the 3D scanning images or 2D photographs. Further, the clothing patterns can be generated by using computer-aided design (CAD) techniques based on the collected dimensions. By presenting the generated clothing patterns in DXF (Drawing Exchange Format), the CNC laser-cutting machine can then cut the fabric into pattern pieces automatically. Finally, by integrating the system with the processes of garment sewing, fitting test and final adjustment, the concept of customized clothing making can be realized. It can not only assure good fitness of the customized clothing but also reduce human efforts, costs, and production time.     

A quasi-physical model for predicting the thermal insulation and moisture vapour resistance of clothing

Based on the improved understanding of the effects of wind and walking motion on the thermal insulation and moisture vapour resistance of clothing induced by air ventilation in the clothing system, a new model has been derived based on fundamental mechanisms of heat and mass transfer, which include conduction, diffusion, radiation and natural convection, wind penetration and air ventilation. The model predicts thermal insulation of clothing under body movement and windy conditions from the thermal insulation of clothing measured when the person is standing in the still air. The effects of clothing characteristics such as fabric air permeability, garment style, garment fitting and construction have been considered in the model through the key prediction parameters. With the new model, an improved prediction accuracy is achieved with a percentage of fit being as high as 0.96.     

面向不同体型特征的服装款式迁移方法

大部分成衣的设计以标准比例的人模作为参照,而对于非标准体型的顾客,标码 服装的大小尺寸则很难与之进行有效匹配。基于此,提出了一种面向不同体型特征的服装款 式迁移方法。首先,对于一批不同款式的服装,通过物理模拟的方式穿着到标模及非标模人 体之上,形成标模试穿的服装实例及非标模试穿的服装实例;其次,使用仿射变换表示同款 服装在标模及非标模下服装实例间的变形映射,并借助主成分分析法求解服装变形,在保留 因体型特征导致的服装形变的基础上,剔除由服装款式信息引起的服装形变;最后,将服装 变形用于标模到非标模的服装款式迁移,并使用平均离散曲率衡量迁移前后服装款式的改变 程度。实验结果表明,迁移后的服装携带了标模服装的款式信息,并保留了非标模服装的体 型特征。     

A template of ease allowance for garments based on a 3D reverse methodology

Nowadays, with the very competitive environment and the emergence of the mass customization concept, garment design is become a crucial step for clothing companies. Indeed, garments should be designed quickly under controlled costs and perfectly fitted to consumers. No traditional pattern making in 2D matches these new constraints since it requires the production of expensive and time consuming physical prototypes. The study presented in this paper relies on 3D virtual design that lets achieve customized garments. An original image processing method based on ellipses and a reverse methodology using scans of a reference body and garment enables evaluation of the overall 3D ease of the garment. The garment is then designed virtually and the result is checked with a 3D simulation on the reference body.     

基于草图交互的个性化服装生成方法

以建立和交互修改三维服装草图为设计手段,提出在三维人体模型上生成三维个性化服装的参数化造型方法．服装草图由2种基本几何元素组成：体现人体围度信息的封闭样条曲线和体现人体在高度方向上曲面形状过渡的不封闭样条曲线．将服装草图约束分为2类4种：一类是体现服装宽松程度的人体与服装曲面之间的间隙约束;另一类是服装几何元素本身之间的共点、共面与对称约束．从人体的特征点出发,通过间隙约束生成服装草图的几何元素;在共点、对称和共面约束下,由服装草图几何元素建立拓扑结构为四边网格的服装草图．服装草图的交互修改是草图约束维护的过程,构建侧视图、正视图、断面图3个视图组成草图修改平台,在平台上交互编辑特征曲线．服装曲面则以三维草图为框架,通过对四边网格双线性Coons曲面插值生成．提供的设计方法使服装的造型变得简洁、灵活．     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On

We present a learning-based approach for virtual try-on applications based on a fully convolutional graph neural network. In contrast to existing data-driven models, which are trained for a specific garment or mesh topology, our fully convolutional model can cope with a large family of garments, represented as parametric predefined 2D panels with arbitrary mesh topology, including long dresses, shirts, and tight tops. Under the hood, our novel geometric deep learning approach learns to drape 3D garments by decoupling the three different sources of deformations that condition the fit of clothing: garment type, target body shape, and material. Specifically, we first learn a regressor that predicts the 3D drape of the input parametric garment when worn by a mean body shape. Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape. Finally, we predict fine-scale details such as wrinkles that depend mostly on the garment material. We qualitatively and quantitatively demonstrate that our fully convolutional approach outperforms existing methods in terms of generalization capabilities and memory requirements, and therefore it opens the door to more general learning-based models for virtual try-on applications.