P.Y. Mok

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.

A robust adaptive clustering analysis method for automatic identification of clusters

Identifying the optimal cluster number and generating reliable clustering results are necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis relies not only on the assumption of cluster number but also on the clustering algorithm employed. This paper proposes a new clustering analysis method that identifies the desired cluster number and produces, at the same time, reliable clustering solutions. It first obtains many clustering results from a specific algorithm, such as Fuzzy C-Means (FCM), and then integrates these different results as a judgement matrix. An iterative graph-partitioning process is implemented to identify the desired cluster number and the final result. The proposed method is a robust approach as it is demonstrated its effectiveness in clustering 2D data sets and multi-dimensional real-world data sets of different shapes. The method is compared with cluster validity analysis and other methods such as spectral clustering and cluster ensemble methods. The method is also shown efficient in mesh segmentation applications. The proposed method is also adaptive because it not only works with the FCM algorithm but also other clustering methods like the k-means algorithm.

Block pattern generation: From parameterizing human bodies to fit feature-aligned and flattenable 3D garments

Research on clothing related CAD is blooming rapidly in the last two decades. It speeds up the product development process significantly and shortens the time to market of fashion products. Although many important results have been obtained, particularly in the computer graphics community, the textile industry is somehow reluctance to adopt these results in actual apparel manufacturing. The main concern is the accuracy of the resulted patterns, because the pattern generation processes ignored some important textile material and manufacturing constraints. This paper introduces a method for generating 2D block patterns from 3D scanned body. A parameterization process is first conducted on a scanned body to create a parameterized model, represented by horizontal B-spline curves. A basic wire-frame aligned with body features is then established based on the parameterized model. Proper clothing ease is carefully incorporated into the model by scaling the wireframe to accomplish the desired fit. Based on the deformed wireframe, a 3D flattenable garment is modeled by boundary triangulation. The main contribution of the proposed method is that the created 3D garment blocks are geometrically flattenable to produce accurate 2D patterns with optimized ease distribution to ensure garment fit. The proposed method is validated and compared to two conventional block patternmaking methods. The experimental results indicate that the proposed method is easy to implement and can generate patterns with satisfactory fit. Furthermore, the method can be used to create fit-ensured mass-customized apparel product.

Genetic optimization of JIT operation schedules for fabric-cutting process in apparel manufacture

Fashion products require a significant amount of customization due to differences in body measurements, diverse preferences on style and replacement cycle. It is necessary for today’s apparel industry to be responsive to the ever-changing fashion market. Just-in-time production is a must-go direction for apparel manufacturing. Apparel industry tends to generate more production orders, which are split into smaller jobs in order to provide customers with timely and customized fashion products. It makes the difficult task of production planning even more challenging if the due times of production orders are fuzzy and resource competing. In this paper, genetic algorithms and fuzzy set theory are used to generate just-in-time fabric-cutting schedules in a dynamic and fuzzy cutting environment. Two sets of real production data were collected to validate the proposed genetic optimization method. Experimental results demonstrate that the genetically optimized schedules improve the internal satisfaction of downstream production departments and reduce the production cost simultaneously.

Multiple-objective genetic optimization of the spatial design for packing and distribution carton boxes

Packing and cutting problems, which dealt with filling up a space of known dimension with small pieces, have been an attractive research topic to both industry and academia. Comparatively, the number of reported studies is smaller for container spatial design, i.e., defining the optimal container dimension for packing small pieces of goods with known sizes so that the container space utilization is maximized. This paper aims at searching an optimal set of carton boxes for a towel manufacturer so as to lower the overall future distribution costs by improving the carton space utilization and reducing the number of carton types required. A multi-objective genetic algorithm (MOGA) is used to search the optimal design of carton boxes for a one-week sales forecast and a 53-week sales forecast. Clustering techniques are then used to study the order pattern of towel products in order to validate the genetically generated results. The results demonstrate that MOGA effectively search the best carton box spatial design to reduce unfilled space as well as the number of required carton types. It is important to note that the proposed methodology for optimal container design is not limited to the apparel industry but practically attractive and applicable to every industry which aims for distribution costs reduction.

An adaptive multi-parameter based dispatching strategy for single-loop interbay material handling systems

The automation of interbay systems in 300mm semiconductor wafer fabrication systems (SWFSs) is complex due to the dynamic, stochastic and mass transportation demands, transportation deadlocks, and vehicle blockages. An adaptive multi-parameter based (AMP) dispatching policy is proposed to obtain better performance of the interbay material handling systems and SWFSs. The system parameters, including vehicles distance, lots due date, lots waiting time, and lots origin-destination buffer status parameters are simultaneously considered, and the multi-parameters weight coefficients are adjusted adaptively by Takagi-Sugeno fuzzy logic method. With experimental data from an interbay system of 300mm SWFSs and running simulation experiments, it is demonstrated that the proposed approach has better performance in terms of cycle time, throughput, due-date satisfaction rate, and vehicle utilization compared to conventional single- and multi-attribute dispatching methodologies.

An efficient human model customization method based on orthogonal-view monocular photos

Human body modeling is a central task in computer graphics. In this paper, we propose an intelligent model customization method, in which customers detailed geometric characteristics can be reconstructed using limited size features extracted from the customers orthogonal-view photos. To realize model customization, we first propose a comprehensive shape representation to describe the geometrical shape characteristics of a human body. The shape representation has a layered structure and corresponds to important feature curves that define clothing size. Next, we identify and model a novel relationship model between 2D size features and 3D shape features for each cross-section using real subject scanned data. We predict a customers cross-sectional 3D shape based on size features extracted from the customers photos, and then we reconstruct the customers shape representation using predicted cross-sections. We develop a new deformation algorithm that deforms a template model into a customized shape using the reconstructed 3D shape representation. A total of 30 subjects, male and female, with varied body shapes have been recruited to verify the model customization method. The customized models show high degree of resemblance of the subjects, with accurate body sizes; the accuracy of the models is comparable to scan. It shows that the method is a feasible and efficient solution for human model customization that fulfills the specific needs of the clothing industry.

Solving the two-dimensional irregular objects allocation problems by using a two-stage packing approach

Packing problems are combinatorial optimization problems that concern the allocation of multiple objects in a large containment region without overlap and exist almost everywhere in real world. Irregular objects packing problems are more complex than regular ones. In this study, a methodology that hybridizes a two-stage packing approach based on grid approximation with an integer representation based genetic algorithm (GA) is proposed to obtain an efficient allocation of irregular objects in a stock sheet of infinite length and fixed width without overlap. The effectiveness of the proposed methodology is validated by the experiments in the apparel industry, and the results demonstrate that the proposed method outperforms the commonly used bottom-left (BL) placement strategy in combination with random search (RS).

Determination of fault-tolerant fabric-cutting schedules in a just-in-time apparel manufacturing environment

In apparel manufacturing, accurate upstream fabric-cutting planning is crucial for the smoothness of downstream sewing operations. Effective and reliable fabric-cutting schedules are difficult to obtain because the apparel manufacturing environment is fuzzy and dynamic. In this paper, genetic algorithms and fuzzy-set theory are used to generate fault-tolerant fabric-cutting schedules in a just-in-time production environment. The proposed method is demonstrated by two cases with production data collected from a Hong Kong-owned garment production plant in China. Results of the two cases preliminarily show that the genetically improved fault-tolerant schedules effectively satisfy the demand for downstream production units, guarantee consistent and reliable system performance, and also reduce production costs through reduced operator idle time. More cases will be conducted in order to further validate the effectiveness of the proposed method.

Intelligent production planning for complex garment manufacturing

Apparel production is characterised by labour-intensive manual operations, frequent style changes, seasonal demand and shortening production lead times. With fierce competition worldwide, many manufacturers are switching their production from mass mode to lean mode to shorten their response time to changes. In a complex mixed mode production environment, it is very important to allocate job orders to suitable production lines so as to ensure the effective utilization of production resources and on-time completion of all job orders. In this paper, planning algorithms are proposed for automatic job allocations based on group technology and genetic algorithms. For genetic algorithms based intelligent planning algorithms, single-run and multiple-run genetic algorithms are suggested. Real production data are used to validate the proposed method. The proposed algorithms has been shown being able to substantially improve planning quality. These planning algorithms are currently used by apparel manufacturers in Hong Kong as part of their routine planning operations.