Garment Shape Research Articles

The influence of garment quality evaluation on fashion innovation of dressmakers in Ghana

Purpose The innovativeness of dressmakers is a concern to respondents to satisfy their clothing needs. The purpose of this study is to determine the criteria that respondents use to judge the quality of clothing and its influences on the innovative ability of dressmakers in the clothing manufacturing industries. Design/methodology/approach Three hundred and ninety-seven (397) respondents in the Takoradi Metropolis of Ghana filled out a questionnaire, and the results were used to compile data for the study. The sample size was calculated using Miller and Brewer formula. The data was analysed using structural equational modelling with the SmartPLS v.4 software. Findings The results showed that respondents are very interested in the calibre of clothing produced by their dressmakers. However, the study revealed that when evaluating the quality of a garment, respondents do not simply accept what has been sewn for them but also consider the performance, appearance, fit and shape of the garment. Findings revealed that there was a significant relationship between garment appearance quality (t = 2.605; p < 0.05), garment performance quality (t = 3.915; p < 0.05), garment shape quality (t = 6.248; p <0.05) and fashion innovations. Subsequently, the evaluation of garment fit quality by respondents revealed it does not bring about innovations (t = 1.310; p > 0.05). Practical implications The continuous evaluation of custom-made clothing from customers will go a long way towards highlighting the relative criteria they use to evaluate the innovation of dressmakers. This will help improve the creativity of the dressmakers since such feedback will help them understand and innovate their production skills to meet the preferences of customers. Originality/value The present study provides an in-depth understanding of how garment quality evaluation by customers influences the innovation of dressmakers in Takoradi, Ghana. The constructs were developed for the study to capture the appropriate data from customers for the study. This presents an evaluation criterion on four garment quality variables imperative for use or modification by other studies.

Generating diverse clothed 3D human animations via a generative model

Data-driven garment animation is a current topic of interest in the computer graphics industry. Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence, and garment deformation, but it is difficult to quickly generate diverse clothed human animations. We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label. At the heart of our method is a two-stage strategy. Specifically, we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder (Transformer-CVAE). Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder–decoder architecture. Since the learned latent space comes from varied human motions, our method can generate a variety of styles of motions given a specific motion label. By means of a novel beginning of sequence (BOS) learning strategy and a self-supervised refinement procedure, our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency. We verify our ideas experimentally. This is the first generative model that directly dresses human animation.

Parametric Linear Blend Skinning Model for Multiple-Shape 3D Garments.

We present a novel data-driven Parametric Linear Blend Skinning (PLBS) model meticulously crafted for generalized 3D garment dressing and animation. Previous data-driven methods are impeded by certain challenges including overreliance on human body modeling and limited adaptability across different garment shapes. Our method resolves these challenges via two goals: 1) Develop a model based on garment modeling rather than human body modeling. 2) Separately construct low-dimensional sub-spaces for modeling in-plane deformation (such as variation in garment shape and size) and out-of-plane deformation (such as deformation due to varied body size and motion). Therefore, we formulate garment deformation as a PLBS model controlled by canonical 3D garment mesh, vertex-based skinning weights and associated local patch transformation. Unlike traditional LBS models specialized for individual objects, PLBS model is capable of uniformly expressing varied garments and bodies, the in-plane deformation is encoded on the canonical 3D garment and the out-of-plane deformation is controlled by the local patch transformation. Besides, we propose novel 3D garment registration and skinning weight decomposition strategies to obtain adequate data to build PLBS model under different garment categories. Furthermore, we employ dynamic fine-tuning to complement high-frequency signals missing from LBS for unseen testing data. Experiments illustrate that our method is capable of modeling dynamics for loose-fitting garments, outperforming previous data-driven modeling methods using different sub-space modeling strategies. We showcase that our method can factorize and be generalized for varied body sizes, garment shapes, garment sizes and human motions under different garment categories.

Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Constraints.

Geometry- and appearance-controlled full-body human image generation is an interesting but challenging task. Existing solutions are either unconditional or dependent on coarse conditions (e.g., pose, text), thus lacking explicit geometry and appearance control of body and garment. Sketching offers such editing ability and has been adopted in various sketch-based face generation and editing solutions. However, directly adapting sketch-based face generation to full-body generation often fails to produce high-fidelity and diverse results due to the high complexity and diversity in the pose, body shape, and garment shape and texture. Recent geometrically controllable diffusion-based methods mainly rely on prompts to generate appearance. It is hard to balance the realism and the faithfulness of their results to the sketch when the input is coarse. This work presents Sketch2Human, the first system for controllable full-body human image generation guided by a semantic sketch (for geometry control) and a reference image (for appearance control). Our solution is based on the latent space of StyleGAN-Human with inverted geometry and appearance latent codes as input. Specifically, we present a sketch encoder trained with a large synthetic dataset sampled from StyleGAN-Human's latent space and directly supervised by sketches rather than real images. Considering the entangled information of partial geometry and texture in StyleGAN-Human and the absence of disentangled datasets, we design a novel training scheme that creates geometry-preserved and appearance-transferred training data to tune a generator to achieve disentangled geometry and appearance control. Although our method is trained with synthetic data, it can also handle hand-drawn sketches. Qualitative and quantitative evaluations demonstrate the superior performance of our method to state-of-the-art methods.

Exploring the attractiveness of combinations of natural colors and contemporary shapes in fashion design

In this research, the attractiveness of combinations of natural colors and contemporary shapes in fashion design is explored. This was done by means of a questionnaire survey. Four shapes of clothing and ten colors obtained from plant and insect pigments are proposed. The research was conducted in two phases – qualitative and quantitative analysis. The quantitative study confirmed the results of the qualitative study and using an appropriate data reduction method, the study results were analyzed graphically. The results showed that users rated designs with high-contrast color and shape combinations as more attractive than those with low-contrast combinations. The study also showed that in addition to the shape of the garment, the choice of natural color sources can also influence consumer preferences. This study aims to overcome the shortcomings of previous studies that only examined color without investigating its relationship to garment shape. The knowledge gained from this study can help fashion designers create garments that are both aesthetically pleasing and environmentally friendly. However, further research is needed to determine the relationship between natural colors, contemporary shapes and consumer behavior in selecting them.

DeepCloth: Neural Garment Representation for Shape and Style Editing.

Garment representation, editing and animation are challenging topics in the area of computer vision and graphics. It remains difficult for existing garment representations to achieve smooth and plausible transitions between different shapes and topologies. In this work, we introduce, DeepCloth, a unified framework for garment representation, reconstruction, animation and editing. Our unified framework contains 3 components: First, we represent the garment geometry with a "topology-aware UV-position map", which allows for the unified description of various garments with different shapes and topologies by introducing an additional topology-aware UV-mask for the UV-position map. Second, to further enable garment reconstruction and editing, we contribute a method to embed the UV-based representations into a continuous feature space, which enables garment shape reconstruction and editing by optimization and control in the latent space, respectively. Finally, we propose a garment animation method by unifying our neural garment representation with body shape and pose, which achieves plausible garment animation results leveraging the dynamic information encoded by our shape and style representation, even under drastic garment editing operations. To conclude, with DeepCloth, we move a step forward in establishing a more flexible and general 3D garment digitization framework. Experiments demonstrate that our method can achieve state-of-the-art garment representation performance compared with previous methods.

Bacterial cellulose grown from kombucha: Assessment of textile performance properties using fashion apparel tests

Bacterial cellulose (BC) has been suggested as a sustainable alternative textile for apparel. Previous studies have evaluated the production of BC sheets and the suitability of these to form garment shapes. The laboratory measured physical performance characteristics of BC from an apparel perspective remain relatively unexplored. The aim of this study was to produce reproducible sheets of BC, enabling the evaluation of the performance of the BC in an apparel textile testing context, and comparison to other textile materials. Grown in sterile black tea with glucose, the BC presented as a mesh of non-woven nanofibers, and thus comparison was made with three non-woven fabrics. It has also been suggested that BC could be used as ‘vegetable’ leather; therefore, performance comparisons were conducted with animal skins. Utilizing British, European and International standard test methods, the selected fabrics were evaluated for their performance in tensile, elongation, moisture vapor permeability and abrasion tests, relevant for an apparel end-use. Tensile strength testing revealed that BC is weaker than its animal counterparts but does display similar physical characteristics at the point of failure; however, it displayed a higher tensile strength than the non-woven fabrics chosen for comparison. BC was the least breathable and most moisture-retentive of all the fabrics tested, raising questions regarding its suitability and comfort for apparel applications in its untreated state. However, BC displayed superior performance when tested for resistance to abrasion, suggesting it could be best utilized in the form of encapsulated patches in items subjected to this type of damage.

Designing Personalized Garments with Body Movement

AbstractThe 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.

Variational Auto-Encoder for 3D Garment Deformation Prediction

Traditional garment animation workflow relies on the professional clothing simulator, which requires manual editing of artists or animators. There is no doubt that such a process is time-consuming and laborious. Synthesizing garment dynamics according to the input high-level parameters in a semi-automatic way not only helps dismiss the domain gap between inspiration and technical implementation, but also enables artists to focus on the authoring of animating contents. To that end, a variational auto-encoder-based garment animation synthesis method is presented. Firstly, a set of motion sequences composed of different poses are sampled to generate the human body dataset. Secondly, a variational auto-encoder network is constructed to learn the probabilistic distribution of clothing deformation from garment motions under different pose variations. Besides, a mesh Laplacian term on the loss function is introduced to preserve wrinkle details of the synthesized garments. After that, constraints on the latent space are imposed to control the garment shape to be generated. Finally, a refinement process is employed to resolve the penetration between the body surface and garment mesh, obtaining realistic clothing deformations. Proposed method is qualitatively and quantitatively evaluated on the AMASS dataset from different aspects: body motion/shape-driven garment synthesis, garment animation authoring. The experimental results demonstrate that proposed workflow is able to produce visually realistic garments without noticeable artifacts. Proposed method can produce temporally-consistent garment dynamics with shape and pose variations, which assists artists in authoring the desired clothing deformations.

NeuralTailor

The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world. One critical task is the disentanglement of the intrinsic garment shape from deformations due to fabric properties, physical forces, and contact with the body. We propose to use a garment sewing pattern, a realistic and compact garment descriptor, to facilitate the intrinsic garment shape estimation. Another major challenge is a high diversity of shapes and designs in the domain. The most common approach for Deep Learning on 3D garments is to build specialized models for individual garments or garment types. We argue that building a unified model for various garment designs has the benefit of generalization to novel garment types, hence covering a larger design domain than individual models would. We introduce NeuralTailor, a novel architecture based on point-level attention for set regression with variable cardinality, and apply it to the task of reconstructing 2D garment sewing patterns from the 3D point cloud garment models. Our experiments show that NeuralTailor successfully reconstructs sewing patterns and generalizes to garment types with pattern topologies unseen during training.

A Continuous Robot Vision Approach for Predicting Shapes and Visually Perceived Weights of Garments

We present a continuous perception approach that learns geometric and physical similarities between garments by continuously observing a garment while a robot picks it up from a table. The aim is to capture and encode geometric and physical characteristics of a garment into a manifold where a decision can be carried out, such as predicting the garment’s shape class and its visually perceived weight. Our approach features an early stop strategy, which means that a robot does not need to observe a full video sequence of a garment being picked up from a crumpled to a hanging state to make a prediction, taking 8 seconds in average to classify garment shapes. In our experiments, we find that our approach achieves prediction accuracies of 93% for shape classification and 98.5% for predicting weights and advances state-of-art approaches in similar robotic perception tasks by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,22\%$</tex-math></inline-formula> for shape classification.

Computational pattern making from 3D garment models

We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.

FREDDY SPA v HUGZ CLOTHING LTD

Abstract H1 Patents — Unregistered designs — Passing off — Breach of settlement agreement — Clothing — Shapewear jeans — Validity — Infringement — Claim construction — Novelty — Obviousness — Goodwill — Product appearance — Post-sale confusion — Subsistence of unregistered design right — Features of shape and configuration — Shape of garment “as worn” — Method or principle of construction — Must fit — Must match — Surface decoration — Licence of right

Development of three-dimensional clothing model for a computer-simulated person integrated with a thermoregulation model

In recent years, an integrated analysis of computational fluid dynamics (CFD) and computer simulation person (CSP), especially to reproduce the shape of the human body, has been conducted to estimate the interaction between the human body and its surrounding indoor environment. Meanwhile, clothing is often treated in a simplified manner, as a means of resistance to heat and pollutant transfer, and there is sufficient room for improvement in the hygrothermal and scalar transfer phenomena in and around clothing with a complex geometry. In this study, some garment models with complex geometry and others with simplified geometry were created with a CSP, and airflow, temperature, and humidity were investigated along with the CSP. It was assumed that only heat and water vapor were transported in the garment. As a result, the naked model was found to be over-or underestimated with respect to all airflow, temperature, and water vapor. It was also found that models with a simple garment shape produced the same results as models with a complex geometry on a macroscopic scale. Models with different regions and smaller air gaps between the clothes and the human body should be confirmed.

Knit sketching

We present a novel workflow to design and program knitted garments for industrial whole-garment knitting machines. Inspired by traditional garment making based on cutting and sewing, we propose a sketch representation with additional annotations necessary to model the knitting process. Our system bypasses complex editing operations in 3D space, which allows us to achieve interactive editing of both the garment shape and its underlying time process. We provide control of the local knitting direction, the location of important course interfaces, as well as the placement of stitch irregularities that form seams in the final garment. After solving for the constrained knitting time process, the garment sketches are automatically segmented into a minimal set of simple regions that can be knitted using simple knitting procedures. Finally, our system optimizes a stitch graph hierarchically while providing control over the tradeoff between accuracy and simplicity. We showcase different garments created with our web interface.

EXPERIMENTAL STUDIES OF BENDING CHARACTERISTICS FOR SELECTION OF MODERN OF STIFFENING FABRICS

The article presents the results of studies of the bending characteristics of modern stiffening fabrics of different fibrous composition. The studies were carried out by an automated method that allows im-plementing a graphical record of the bending process and recovery after bending using specially developed software. The method allows determining not only standard, but also new bending characteristics. New quality indicators, which determine the ability of stiffening fabrics to be processed into high-quality stiffening pads and allow assessing the garment shape stability during operation, are proposed. The expediency of experimental evaluation of these indicators for the modern range of stiffening fabrics is established. The reference data on indicators of technological properties of stiffening fabrics allowing digitalising selection is given. A new algorithm for the rational choice of shape-stable fabrics is proposed, focused on new characteristics of bending – the structure stability coefficient and the difference in work.

Analysis on influencing factors of X-shape dress silhouette based on eye-tracking technology

For a long time, the research of inteligent garment pattern making has become a difficulty in the research of garment science, which involves the summary of the rules of garment pattern making and the accurate control of the shape of garment. In this paper, eye-tracking technology is adopted to analyze the influencing of factors of the silhouette of X-shape dress. Multivariate analysis of variance and average of participants’ eye-tracking were conducted for different design styles and interest areas of the X-shape dress by using the five interest area’s Fixation Duration, Visit Count and other indicators. According to the research, the influencing factors of X-shape dress silhouette are waist, chest, hem, hip and shoulder. Therefore, it is of great significance to take waist measurement, chest measurement and hem as the main factors affecting the X-shape dress silhouette, and to further study of the relationship between the structure and X-shape dress silhouette, so as to realize intelligent rapid pattern making based on computer technology.

3D interactive garment parametric pattern-making and linkage editing based on constrained contour lines

PurposeThe purpose of this paper is to propose an interactive 2D–3D garment parametric pattern-making and linkage editing scheme that integrates clothing design, simulation and interaction to design 3D garments and 2D patterns. The proposed scheme has the potential to satisfy the individual needs of fashion industry, such as precise fit evaluation of the garment, interactive style editing with ease allowance and constrained contour lines in fashion design.Design/methodology/approachThe authors first construct a parametric pattern-making model for flat pattern design corresponding to the body dimensions. Then, the designing 2D patterns are stitched on a virtual 3D mannequin by performing a virtual try-on. If the customer is unsatisfied after the virtual try-on, the adjustable parameters (appearance parameters and fit parameters) can be adjusted using the 2D–3D linkage editing with hierarchical constrained contour lines, and the fit evaluation tool interactively provides the feedback.FindingsThe authors observed that the usability and efficiency of the existing garment pattern-making method simplifies the garment pattern-making process. The authors utilize an interactive garment parametric flat pattern-making model to generate an individualized garment flat pattern that effectively adjust and realize the local editing of the garment pattern-making. The 2D–3D linkage editing is then employed, which alters the size and shape of garment pattern for a precise human model fit of the 3D garment using hierarchical constrained contour lines. Various instances have validated the effectiveness of the proposed scheme, which can increase the reusability of the existing garment styles and improve the efficiency of fashion design.Research limitations/implicationsFirst, the authors do not consider the garment pattern-making design of sophisticated styles. Second, the authors do not directly consider complex garment shapes such as wrinkles, folds, multi-layer models and fabric physical properties.Originality/valueThe authors propose a pattern adjustment scheme that uses the 3D virtual try-on technology to avoid repetitions of reality-based fit tests and garment sample making in the designing process of clothing products. The proposed scheme provides interactive selections of garment patterns and sizes and renders modification tools for 3D garment designing and 2D garment pattern-making. The authors present the 2D–3D interactive linkage editing scheme for a custom-fit garment pattern based on the hierarchical constraint contour lines. The spatial relationship among the human body, pattern pieces and 3D garment model is adequately expressed, and the final design result of the garment pattern is obtained by constraint solving. Meanwhile, the tightness tension of different parts of the 3D garment is analyzed, and the fit and comfort of the garment are quantitatively evaluated.

MulayCap: Multi-Layer Human Performance Capture Using a Monocular Video Camera.

We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning. The method uses "multi-layer" representations for geometry reconstruction and texture rendering, respectively. For geometry reconstruction, we decompose the clothed human into multiple geometry layers, namely a body mesh layer and a garment piece layer. The key technique behind is a Garment-from-Video (GfV) method for optimizing the garment shape and reconstructing the dynamic cloth to fit the input video sequence, based on a cloth simulation model which is effectively solved with gradient descent. For texture rendering, we decompose each input image frame into a shading layer and an albedo layer, and propose a method for fusing a fixed albedo map and solving for detailed garment geometry using the shading layer. Compared with existing single view human performance capture systems, our "multi-layer" approach bypasses the tedious and time consuming scanning step for obtaining a human specific mesh template. Experimental results demonstrate that MulayCap produces realistic rendering of dynamically changing details that has not been achieved in any previous monocular video camera systems. Benefiting from its fully semantic modeling, MulayCap can be applied to various important editing applications, such as cloth editing, re-targeting, relighting, and AR applications.

GarNet++: Improving Fast and Accurate Static 3D Cloth Draping by Curvature Loss.

In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes. Our network learns to mimic a physics-based simulation (PBS) method while requiring two orders of magnitude less computation time. To train the network, we introduce loss terms inspired by PBS to produce plausible results and make the model collision-aware. To increase the details of the draped garment, we introduce two loss functions that penalize the difference between the curvature of the predicted cloth and PBS. Particularly, we study the impact of mean curvature normal and a novel detail-preserving loss both qualitatively and quantitatively. Our new curvature loss computes the local covariance matrices of the 3D points, and compares the Rayleigh quotients of the prediction and PBS. This leads to more details while performing favorably or comparably against the loss that considers mean curvature normal vectors in the 3D triangulated meshes. We validate our framework on four garment types for various body shapes and poses. Finally, we achieve superior performance against a recently proposed data-driven method.