Garment Modeling from a Single Image

Modeling of realistic garments is essential for online shopping and many other applications including virtual characters. Most of existing methods either require a multi‐camera capture setup or a restricted mannequin pose. We address the garment modeling problem according to a single input image. We design an all‐pose garment outline interpretation, and a shading‐based detail modeling algorithm. Our method first estimates the mannequin pose and body shape from the input image. It further interprets the garment outline with an oriented facet decided according to the mannequin pose to generate the initial 3D garment model. Shape details such as folds and wrinkles are modeled by shape‐from‐shading techniques, to improve the realism of the garment model. Our method achieves similar result quality as prior methods from just a single image, significantly improving the flexibility of garment modeling.     

Detailed wrinkle generation of virtual garments from a single image

The presence of proper wrinkles is important while modeling realistic virtual garments. Unlike previously used full 3D information methods, our approach achieves detailed garment generation from a single image. First, we retrieve a garment image similar to the initial virtual garment based on content-based image retrieval (CBIR) method. Then, we preprocess the image with a combination of human body reshaping, image segmentation and shape recovery, to obtain the 3D wrinkle details. Finally, the garment height are synthesized into the virtual garment. For better suit the posture of the human body, excess garment energy are released to remove the unmatched wrinkles. We apply our method to various styles of virtual garments, and it enable virtual characters in general pose to be dressed in these garments and complete wrinkle generation. Compared with existing garment modeling methods, the experimental results show that the proposed method could quickly capture the realistic wrinkles of virtual garments with less manual operation and achieve more realistic wrinkles for virtual garments.

     

Designing Personalized Garments with Body Movement

The standardized sizes used in the garment industry do not cover the range of individual differences in body shape for most people, leading to ill-fitting clothes, high return rates and overproduction. Recent research efforts in both industry and academia, therefore, focus on virtual try-on and on-demand fabrication of individually fitting garments. We propose an interactive design tool for creating custom-fit garments based on 3D body scans of the intended wearer. Our method explicitly incorporates transitions between various body poses to ensure a better fit and freedom of movement. The core of our method focuses on tools to create a 3D garment shape directly on an avatar without an underlying sewing pattern, and on the adjustment of that garment's rest shape while interpolating and moving through the different input poses. We alternate between cloth simulation and rest shape adjustment based on stretch to achieve the final shape of the garment. At any step in the real-time process, we allow for interactive changes to the garment. Once the garment shape is finalized for production, established techniques can be used to parameterize it into a 2D sewing pattern or transform it into a knitting pattern.     

DeepGarment : 3D Garment Shape Estimation from a Single Image

3D garment capture is an important component for various applications such as free‐view point video, virtual avatars, online shopping, and virtual cloth fitting. Due to the complexity of the deformations, capturing 3D garment shapes requires controlled and specialized setups. A viable alternative is image‐based garment capture. Capturing 3D garment shapes from a single image, however, is a challenging problem and the current solutions come with assumptions on the lighting, camera calibration, complexity of human or mannequin poses considered, and more importantly a stable physical state for the garment and the underlying human body. In addition, most of the works require manual interaction and exhibit high run‐times. We propose a new technique that overcomes these limitations, making garment shape estimation from an image a practical approach for dynamic garment capture. Starting from synthetic garment shape data generated through physically based simulations from various human bodies in complex poses obtained through Mocap sequences, and rendered under varying camera positions and lighting conditions, our novel method learns a mapping from rendered garment images to the underlying 3D garment model. This is achieved by training Convolutional Neural Networks (CNN‐s) to estimate 3D vertex displacements from a template mesh with a specialized loss function. We illustrate that this technique is able to recover the global shape of dynamic 3D garments from a single image under varying factors such as challenging human poses, self occlusions, various camera poses and lighting conditions, at interactive rates. Improvement is shown if more than one view is integrated. Additionally, we show applications of our method to videos.     

Image warping for retargeting garments among arbitrary poses

We address the problem of warping 2D images of garments onto target mannequins of arbitrary poses. The motivation for this work is to enable an online shopper to drag and drop selected articles of clothing onto a single mannequin to configure and visualize outfits. Such a capability requires each garment to be available in a pose that is consistent with the target mannequin. A 2D deformation system is proposed, which enables a designer to quickly deform images of clothing onto a target shape with both fine and coarse controls over the deformation. This system has retargeted thousands of images for retailers to establish virtual dressing rooms for their online customers.     

Learning‐Based Animation of Clothing for Virtual Try‐On

This paper presents a learning‐based clothing animation method for highly efficient virtual try‐on simulation. Given a garment, we preprocess a rich database of physically‐based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning‐based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try‐on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results.     

基于弹簧质点模型的二维/三维映射算法*

提出了一种用于服装设计的二维/三维映射算法.该算法基于弹簧质点变形模型,服装裁剪片二维到三维映射及三维到二维映射可以在该模型中得到统一的实现.在服装裁剪片二维到三维的映射过程中,二维裁剪片被放置在人体模型附近的初始位置,在缝合力的作用下,裁剪片自动变形并缝合到人体模型上.在服装裁剪片三维到二维映射的过程中,三维裁剪片被初始映射到指定的平面内,在弹性变形力的作用下,逐步变形并得到最终的二维裁剪片.在进行服装裁剪片二维到三维映射的过程中,考虑了干涉检验的问题.     

Wrinkling Captured Garments Using Space-Time Data-Driven Deformation

The presence of characteristic fine folds is important for modeling realistic looking virtual garments. While recent garment capture techniques are quite successful at capturing the low-frequency garment shape and motion over time, they often fail to capture the numerous high-frequency folds, reducing the realism of the reconstructed space-time models. In our work we propose a method for reintroducing fine folds into the captured models using data-driven dynamic wrinkling. We first estimate the shape and position of folds based on the original video footage used for capture and then wrinkle the surface based on those estimates using space-time deformation. Both steps utilize the unique geometric characteristics of garments in general, and garment folds specifically, to facilitate the modeling of believable folds. We demonstrate the effectiveness of our wrinkling method on a variety of garments that have been captured using several recent techniques.     

3D virtual apparel design for industrial applications

The integration of physics-based models within CAD systems for garment design leads to highly accurate cloth shape results for virtual prototyping and quality evaluation tasks. To this aim, we present a physics-based system for virtual cloth design and simulation expressly conceived for design purposes. This environment should allow the designer to validate her/his style and design option through the analysis of garment virtual prototypes and simulation results in order to reduce the number and role of physical prototypes. Garment shapes are accurately predicted by including material properties and external interactions through a particle-based cloth model embedded in constrained Newtonian dynamics with collision management, extended to complex-shaped assembled and finished garments. Our model is incorporated within a 3D graphical environment, and includes operators monitoring the whole design process of apparel, e.g. panel sewing, button/dart insertion, multi-layered fabric composition, garment finishings, etc. Applications and case studies are considered, with analysis of CAD modelling phases and simulation results concerning several male and female garments.     

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.     

Pattern computation for compression garment by a physical/geometric approach

This paper addresses the problem of computing planar patterns for compression garments. In the garment industry, the compression garment has been increasingly widely used to retain the shape of a human body, where certain strain (or normal pressure) is designed at some places on the compression garment. Variant values and distribution of strain can only be generated by sewing different two-dimensional (2D) patterns and warping them onto the body. We present a physical/geometric approach for computing 2D meshes that, when folded onto the three-dimensional (3D) body, can generate a user-defined strain distribution through proper distortion. This is opposite to the widely studied mesh parameterization problem, whose objective is to minimize the distortion between the 2D and 3D meshes in angle, area or length.     

Interactive virtual try-on clothing design systems

Clothing computer design systems include three integrated parts: garment pattern design in 2D/3D, virtual try-on and realistic clothing simulation. Some important results have been obtained in pattern design and clothing simulation since the 1980s. However, in the area of virtual try-on, only limited methods have been proposed which are applicable to some defined garment styles or under restrictive sewing assumptions. This paper presents a series of new techniques from virtually sewing up complex garment patterns on human models to visualizing design effects through physical-based real-time simulation. We first employ an hierarchy of ellipsoids to approximate human models in which the bounding ellipsoids are optimized recursively. We also present a new scheme for including contact friction and resolving collisions. Four types of user interactive operation are introduced to manipulate cloth patterns for pre-positioning, virtual sewing and later obtaining cloth simulation. In the cloth simulation, we propose a simplified cloth dynamic model and an integration scheme to realize a high quality real-time cloth simulation. We demonstrate the robustness of our proposed systems by complex garment style virtual try-on and cloth simulation.     

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。     

基于SceneLib的三维服装建模技术研究

使用几何建模法，结合传统的表面建模方法，提出了一种改良的曲面片法，成功地建立了人体模型和服装模型。文章基于SceneLib的函数库，实现曲面片建模，并以计算机图形学的知识，对建立的模型进行有效的控制，为建立服装模型提供了一种新的思路。     

Physical simulation of wet clothing for virtual humans

We present a technique that simulates wet garments for virtual humans with realistic folds and wrinkles. Our approach combines three new models to allow realistic simulation of wet garments: (1) a simplified saturation model that modifies the masses, (2) a nonlinear friction model derived from previously reported, real-world measurements, and (3) a wrinkle model based on imperfection sensitivity theory. In contrast to previous approaches to wet cloth, the proposed models are supported by the experimental results reported in the textile literature with parameters varying over the course of the simulation. As a result, the wet garment motions simulated by our method are comparable to that of real wet garments. Our approach recognizes the special, practical importance of contact models with human skin and provides a specific skin-cloth friction solution. We evaluate our approach by draping a rotating sphere and simulating a typical garment on a virtual human in the rain. Both of these examples are typical scenarios within computer graphics research.     

Interactive 3D garment design with constrained contour curves and style curves

This paper presents interactive techniques to design 3D garments conveniently and precisely with constrained contour curves and style curves. Contour curves, including silhouette curves and cross section curves, are used for garment surface modeling. Style curves, including seam lines, dart lines, notch lines and grain lines, are introduced for designing patterns on the triangular garment surfaces. Contour curves are extracted automatically from the boundaries of garment sub-meshes. The definitions and resolving rules of various constraints are introduced for editing the contour curves conveniently. Style curves are generated by projecting control points onto 3D garment triangular surfaces. In order to draw the style curves validly, some constraints are also introduced according to the craft requirements of pattern design. Furthermore, the effects of style curves in pattern flattening are analyzed, which can guide the designer to draw style curves more reasonably and enhance the pattern design quality. Finally, some examples are given to show that our methods can make the 3D garment design more flexible and friendly, and style curves can be applied into design patterns on 3D triangular surface for shoes, toys and so on.     

Garment capture from a photograph

This paper presents a new method which can create the virtual garment from a single photograph of a real garment put on to the mannequin. In solving this problem, we obtain the insight from the pattern drafting theory in the clothing field. We abstract the drafting process into a computer module, which takes the garment type and primary body sizes then produces the draft as the output. Then the problem is reduced to find out the garment type and primary body sizes. We find that information by analyzing the silhouette of the garment with respect to the mannequin. The method works robustly and produces practically usable virtual clothes that can be used for the graphical coordination. Copyright © 2015 John Wiley & Sons, Ltd.