Clothing Shape Research Articles

Symmetrical Siamese Network for pose-guided person synthesis

Pose-Guided Person Image Synthesis (PGPIS) aims to generate a realistic person image that preserves the appearance of the source person while adopting the target pose. Various appearances and drastic pose changes make this task highly challenging. Due to the insufficient utilization of paired data, existing models face difficulties in accurately preserving the source appearance details and high-frequency textures in the generated images. Meanwhile, although current popular AdaIN-based methods are advantageous in handling drastic pose changes, they struggle to capture diverse clothing shapes imposed by the limitation of global feature statistics. To address these issues, we propose a novel Symmetrical Siamese Network (SSNet) for PGPIS, which consists of two synergistic symmetrical generative branches that leverage prior knowledge of paired data to comprehensively exploit appearance details. For feature integration, we propose a Style Matching Module (SMM) to transfer multi-level region appearance styles and gradient information to the desired pose for enriching the high-frequency textures. Furthermore, to overcome the limitation of global feature statistics, a Spatial Attention Module (SAM) is introduced to complement the SMM for capturing clothing shapes. Extensive experiments show the effectiveness of our SSNet, achieving state-of-the-art results on public datasets. Moreover, our SSNet can also edit the source appearance attributes, making it versatile in wider application scenarios.

Enhanced Virtual Try on for Clothing using Deep learning

Abstract: An increasing number of individuals are shopping online, particularly for clothing. However, while purchasing online, actual try-ons are not allowed, which limits the customer’s ability to see how a garment would appear on them. To enable buyers to virtually try on the desired clothing from an online store, an imagebased try-on system was devised. The ability to practically put on several clothes has increased customer interest in shopping. Image Recognition and Feature Extraction was the important part of the model to get desired outcomes. And To address these problems, we implement the CP-VTON+ (Clothing shape and texture Preserving VTON), which performs substan- tially better than the most advanced in sense of both quantitative and qualitative evaluations. The CP-VTON+ model not only adapt to detangle clothes from the person, but also maintains the clothing’s details on the try-on outcomes.

Optimal surface clothing with elastic nets

The clothing problem aims at identifying the shape of a planar fabric for covering a target surface in the three-dimensional space. It poses significant challenges in various applications, ranging from fashion industry to digital manufacturing. Here, we propose a novel inverse design approach to the elastic clothing problem that is formulated as a constrained optimization problem. We assume that the textile behaves as an orthotropic, nonlinear elastic surface with fibers distributed along its warp and weft threads, and we enforce mechanical equilibrium as a variational problem. The target surface is frictionless, except at its boundary where the textile is pinned, imposing a unilateral obstacle condition for the reactive forces at the target surface. The constrained optimization problem also accounts for an elongation condition of the warp and weft fibers, possibly with bounded shearing angle. We numerically solve the resulting constrained optimization problem by means of a gradient descent algorithm. The numerical results are first validated against known clothing solutions for Chebyshev nets, taking the limit of inextensible fibers. We later unravel the interplay between thread and shear stiffness for driving the optimal cloth shape covering the hemisphere and the hemicatenoid. We show how the metric of these target surfaces strongly affects the resulting distribution of the reaction forces. When considering the limit of covering the full sphere, we show how clothing with elastic nets allows to avoid the onset of singularities in the corresponding Chebyshev net, by developing corners at the cloth boundary.

CycleVTON: A Cycle Mapping Framework for Parser-Free Virtual Try-On

Image-based virtual try-on aims to transfer a target clothing onto a specific person. A significant challenge is arbitrarily matched clothing and person lack corresponding ground truth to supervised learning. A recent pioneering work leveraged an improved cycleGAN to enable one network to generate the desired image for another network during training. However, there is no difference in the result distribution before and after the clothing changes. Therefore, using two different networks is unnecessary and may even increase the difficulty of convergence. Furthermore, the introduced human parsing used to provide body structure information in the input also have a negative impact on the try-on result. How to employ a single network for supervised learning while eliminating human parsing? To tackle these issues, we present a Cycle mapping Virtual Try-On Network (CycleVTON), which can produce photo-realistic try-on results by using a cycle mapping framework without the parser. In particular, we introduce a flow constraint loss to achieve supervised learning of arbitrarily matched clothing and person as inputs to the deformer, thus naturally mimicking the interaction between clothing and the human body. Additionally, we design a skin generation strategy that can adapt to the shape of the target clothing by dynamically adjusting the skin region, i.e., by first removing and then filling skin areas. Extensive experiments conducted on challenging benchmarks demonstrate that our proposed method exhibits superior performance compared to state-of-the-art methods.

Resolvendo a relação entre o desempenho do figurino e o corpo a partir da perspectiva da filosofia do corpo

Abstract: With the times’ progress and the growth of culture and art, the clothing industry has ushered in new development opportunities in the market. As its expansion and extension, clothing performance not only has high ornamental value, but also can exist independently as an art. However, in shaping the artistic image of clothing performance, the main focus is on the presentation of external images, lacking the expression of the rhythmic and connotative characteristics of body movements, which not only weakens the visual effect of the performance, but also has an impact on its integrity and continuity. Therefore, understanding the relationship between clothing performance and the body, and promoting their mutual integration and penetration are of great value and significance for the current development of visual art. Body philosophy is a philosophical discipline that explores the body and bodily experiences. It is concerned with the way the body perceives and exists. This can help performers and designers think about how clothing shapes and expresses personal and social identity, and how the diversity and change of identity can be explored through costume performance. In order to deeply study the relationship between clothing performance and body, and improve the effectiveness of clothing performance, this article explains the relationship between body and aesthetics based on the development history, function and value of clothing performance, combined with body philosophy theory. From the perspective of body philosophy, the body is an indispensable factor in costume performance, and costume performance can achieve artistic expression of the body, both of which interact and promote each other. In the development of aesthetic activities, the connection of the two closely has important value for achieving higher levels of visual representation.

Reconstructing Complex Shaped Clothing from a Single Image with Feature Stable Unsigned Distance Fields.

Single-view clothing reconstruction usually relies on topologically fixed clothing templates to reduce the problem complexity, but this strategy also makes the reconstructed clothing shape contours simple and lack diversity. In this paper, we propose a novel clothing reconstruction method to generate complex shape contours and open clothing mesh from a single image. At the heart of our work is an implicit unsigned distance field condition on clothing-oriented and pose-stable spatial shape features to represent the clothing from the image. This feature can provide spatially aligned clothing shape priors to improve the pose robustness. It is based on a type-generic clothing template derived from the mainstream clothing generative model to avoid tedious template design and switching. To output open clothing mesh results from noisy clothing unsigned distance fields, we develop a two-stage clothing mesh extraction method. It takes the point clouds as an intermediate representation and produces smooth, plausible and editable clothing mesh results. To provide effective supervision, we construct a pose-rich and shape-complete clothing scan dataset by enhancing clothing pose diversity and complementing missing clothing geometry caused by occlusion. Extensive experiments demonstrate that our method achieves state-of-the-art levels. More importantly, we provide a simple but effective, and low-cost way to reconstruct complex shape contours clothing from a single image.

An improved image processing algorithm for visual characteristics in graphic design.

Drawing the clothing plan is an essential part of the clothing industry. However, the irregular shape of clothing, strong deformability and sensitivity to light make the fast and accurate realization of clothing image retrieval a very challenging problem. The successful application of the Transformer in image recognition shows the application potential of the Transformer in the image field. This article proposes an efficient and improved clothing plan based on ResNet-50. Firstly, in the feature extraction section, the ResNet-50 network structure embedded in the Transformer module is used to improve the network's receptive field range and feature extraction ability. Secondly, dense jump connections are added to the ResNet-50 upsampling process, making full use of feature extraction information at each stage, further improving the quality of the generated image. The network consists of three steps: the sketch stage, which aims to predict the color distribution of clothing and obtain watercolor images without gradients and shadows. The second is the thinning stage, which refines the watercolor image into a clothing image with light and shadow effect; The third is the optimization stage, which combines the outputs of the first two stages to optimize the generation quality further. The experimental results show that the improved network's IS and first input delay (FID) scores are 4.592 and 1.506, respectively. High-quality clothing images can be generated only by inputting line drawings and a few color points. Compared with the existing methods, the image generated by this network has excellent advantages in realism and accuracy. This method can combine various feature information of images, improve retrieval accuracy, has strong robustness and practicability, and can provide a reference for the daily work of fashion designers.

Preserving cultural heritage through virtual simulation design of traditional clothing styles: a study of Mazu clothing

This study focuses on the virtual simulation design of Mazu clothing, a traditional Chinese style of clothing, using a combination of general design software, clothing CAD software, and clothing 3D design software. The study begins with an introduction to the development and shape of Mazu clothing and analyzes the design elements such as style, structure, color, and material of the clothing. Typical styles of Mazu clothing are then selected, and graphic designs for these styles are created using Corel Draw. The study then moves on to the virtual simulation design process, where the size and specifications of Mazu clothing are set based on the 160/84A standard female body data. Richpeace clothing CAD software is used to draw the pattern of Mazu clothing, completing the pattern design process. Finally, the plate file is imported into CLO 3D software to complete the 3D virtual stitching and simulation design of Mazu clothing. The study finds that the standard error and error probability after design were both below 5%, demonstrating the accuracy of the virtual simulation design method. The research plan presented in this study provides a fast and efficient way to achieve three-dimensional virtual simulation of Mazu clothing, which lays a foundation for the digital design and application of Mazu clothing. Overall, this study provides valuable insights and practical guidance for those interested in virtual simulation design for traditional clothing styles.

Wrinkles Realistic Clothing Reconstruction by Combining Implicit and Explicit Method

We propose a novel method to achieve robust and wrinkle-realistic clothing reconstruction from a single RGB image. Our approach successfully exploits both advantages of implicit function methods and explicit clothing generative models. The former allows to capture high-frequency wrinkle details and clothing appearance from a single image, and the latter provides reasonable clothing shape prior and structured topology. To utilize the implicit function method to obtain implicit clothing, the key is that we need to automatically segment the clothing region from the reconstructed results and solve its depth ambiguity problem. Therefore, we design a pixel-aligned segmentation method to achieve automatic and complete clothing segmentation. It relies on a lightweight clothing mask network and a simple but effective segmentation strategy. To deal with the depth ambiguity problem, a depth correction function is introduced to significantly remove the uncertainty in the depth direction and recover the correct clothing shape. Further, we utilize explicit clothing generative models to provide reasonable shapes and topologies. It first infers an initial explicit clothing template based on generative models. Then, the implicit clothing is fitted by this explicit template to capture high-frequency deformation, resulting in wrinkle-realistic and structured clothing results. Extensive experiments have proved the validity of our approach with the visual effect and geometry accuracy both reaching the state-of-the-art level in clothing reconstruction from a single image.

WOMEN'S CLOTHES AND DECORATIONS OF THE CITY OF NAKHCHIVAN

The land of Azerbaijan is divided into various historical and ethnographic regions in terms of their unique customs and traditions. One of such historical and ethnographic regions is Nakhchivan. In the XIX-XX centuries, this region consisted of two large cities - Nakhchivan and Ordubad. The article explores women's clothing, jewelry and decor items typical for the Nakhchivan region of Azerbaijan. National clothes are an important ethnographic phenomenon that characterizes, distinguishes and conveys the national aspect as a kind of material culture. Like other areas of material culture, clothing is also related to the socio-economic conditions of human life and clearly reflects the labor activity of people, artistic and aesthetic taste and class differences between social groups of society. In addition to religious beliefs, climatic, geographical and natural conditions also influence the style of clothing. The study of folk costumes in historical and ethnographic regions determines the general (similar) and local (different) characteristics of the Azerbaijani clothing culture and helps to closely monitor ethnocultural ties with the neighboring peoples of Azerbaijan. It is clear from research that although the clothing style of the 19th century is a logical continuation of the types of clothing formed in previous historical periods in accordance with the style method and sewing technique, it has undergone changes in accordance with the needs of the new period. This feature is most often found in urban and professional clothing. In particular, the deepening of the division of labor led to the creation of various types of professional clothing. The growth of trade union differences in society over time manifested itself in their clothing. Researchers note that in the 19th century, changes took place in Azerbaijani clothing, one of which was the gradual disappearance of military clothing and related types and their disappearance from the market over time. Only the dagger hanging from the belt and lead pockets on the side of the rope remain in addition to the clothes. After that, these elements partially lost their functional significance, which was preserved as a decorative element of decor Clothes are usually made from cloth. Fabric is one of the elements that reflect the culture of people. The patterns and colors of fabrics are important features that make the difference both between people and between rich and poor.   Since the middle of the 20th century, as in any clothing, national features began to disappear in women's clothing over time. Changes to the shape and features of clothing are now reflected in the style of clothing. Today, national clothes are practically not used among the townspeople. In particular, the media and the socio-economic structure form the basis of the dress change. In addition, the death of older people who wore traditional clothes and the interest of young people in today's fashion have led to the fact that national clothes are not in demand and are on the verge of extinction every day. To prevent this, it is necessary to create the necessary infrastructure for the transfer of these clothes to future generations.

A systematic automated grasping approach for automatic manipulation of fabric with soft robot grippers

PurposeThe garment industry will be one of the major beneficiaries of advances in smart manufacturing, as it is highly labor-intensive and heavily depends on labor force. Manipulating robots in human environments has made great strides in recent years. However, the main research has focused on rigid, solid objects and core capabilities such as grasping, placing remain a challenging problem when dealing with soft textiles. The experimental results indicate that adopting the proposed bionic soft finger will provide garment manufacturers with smart manufacturing capabilities. Then, the purpose of this paper is to utilize the flexibility of the soft finger to transfer fabric layer by layer without damage in garment automation.Design/methodology/approachIn this paper, a new way to separate layer by layer pieces of fabric has been inspired by the rise of soft robotics and their applications in automation. Fabric gripping is accomplished by wiping deformation and pinching the fabric. A single fabric piece is separated from cutting pile by the soft finger in four steps: making an arch by pressing, wiping deformation, grasping and separating, and placing.FindingsThe case study demonstrated that the soft finger arrangement for automated grasping of fabric pieces of a garment can be successfully applied to delicate fabric. A combination of cloth shape and weight determines the number of soft fingers. In addition, the soft finger was tested on different types of fabrics to determine its performance and application capabilities. The technology may be used to produce clothing intelligently in the future, such as intelligent stacking, intelligent transportation and intelligent packaging, to increase clothing industry productivity.Originality/valueAn industrial bionic soft finger gripping system is proposed in this paper for application in the field of fabric automatic manipulation. A piece of fabric could be picked up and released layer by layer from a stack by the proposed gripper without creating any damage to it. Soft grippers have the right proportion of softness and rigidity like a human being. A soft finger has a potential affinity for soft materials such as fabrics without damaging either their surface or their properties.

Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention

Sequential multi-step cloth manipulation is a challenging problem in robotic manipulation, requiring a robot to perceive the cloth state and plan a sequence of chained actions leading to the desired state. Most previous works address this problem in a goal-conditioned way, and goal observation must be given for each specific task and cloth configuration, which is not practical and efficient. Thus, we present a novel multi-step cloth manipulation planning framework named Foldformer. Foldformer can complete similar tasks with only a general demonstration and utilize a space-time attention mechanism to capture the instruction information behind this demonstration. We experimentally evaluate Foldsformer on four representative sequential multi-step manipulation tasks and show that Foldsformer significantly outperforms state-of-the-art approaches in simulation. Foldformer can complete multi-step cloth manipulation tasks even when configurations of the cloth (e.g., size and pose) vary from configurations in the general demonstrations. Furthermore, our approach can be transferred from simulation to the real world without additional training or domain randomization. Despite training on rectangular clothes, we also show that our approach can generalize to unseen cloth shapes (T-shirts and shorts).

Robust and automatic clothing reconstruction based on a single RGB image

We propose a novel method to reconstruct style-matched and texture-realistic clothing from a single image. Our work mainly addresses two challenging problems in clothing reconstruction: (1) how to automatically generate style-consistent geometric clothing models from a single image; (2) how to generate realistic clothing texture when suffering from significant pose estimation error. For the former (geometry reconstruction), we propose a coarse-to-fine approach to empower TailorNet to process image inputs and generate style-matched clothing. The key is that our two-stage method can predict clothing style parameters (TailorNet’s latent vectors indicating clothing shape) from images. We first use an attribute prediction network to identify the gender, clothing type, and style categories from the input. Then, we propose a tightness table-based estimation method to obtain specific clothing style parameter values. For the latter (textures generation), we design a multi-information fusion approach to deal with ghosting issue caused by pose estimate errors. The method utilizes semantic and intrinsic texture information to effectively deal with wrong color sampling in the image. Extensive experiments demonstrate that our method has reached the state-of-the-art level and realized the realistic body and clothing reconstruction from a single RGB image.

Effective cloth folding trajectories in simulation with only two parameters.

Robotic cloth folding remains challenging for robots due to its highly deformable nature. In order to deal with these deformations, several strategies with varying amounts of adaptability have been proposed. We study robotic cloth folding by simulating and evaluating a trajectory search space with only two parameters: one parameter for the trajectory's height and one parameter to tilt it. We extensively analyzed folding a long-sleeved shirt in a high-fidelity simulator. To demonstrate that the trajectory is sufficiently adaptable and robust, we test several cloth shapes, cloth materials, an entire folding sequence and different folding velocities. We can deal with every folding scenario by tuning the two parameters correctly. The trajectories' simplicity and their robustness in simulation make them ideal candidates for future transfer to real-world robotic setups.

Text2Human

Generating high-quality and diverse human images is an important yet challenging task in vision and graphics. However, existing generative models often fall short under the high diversity of clothing shapes and textures. Furthermore, the generation process is even desired to be intuitively controllable for layman users. In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation. We synthesize full-body human images starting from a given human pose with two dedicated steps. 1) With some texts describing the shapes of clothes, the given human pose is first translated to a human parsing map. 2) The final human image is then generated by providing the system with more attributes about the textures of clothes. Specifically, to model the diversity of clothing textures, we build a hierarchical texture-aware codebook that stores multi-scale neural representations for each type of texture. The codebook at the coarse level includes the structural representations of textures, while the codebook at the fine level focuses on the details of textures. To make use of the learned hierarchical codebook to synthesize desired images, a diffusion-based transformer sampler with mixture of experts is firstly employed to sample indices from the coarsest level of the codebook, which then is used to predict the indices of the codebook at finer levels. The predicted indices at different levels are translated to human images by the decoder learned accompanied with hierarchical codebooks. The use of mixture-of-experts allows for the generated image conditioned on the fine-grained text input. The prediction for finer level indices refines the quality of clothing textures. Extensive quantitative and qualitative evaluations demonstrate that our proposed Text2Human framework can generate more diverse and realistic human images compared to state-of-the-art methods. Our project page is https://yumingj.github.io/projects/Text2Human.html. Code and pretrained models are available at https://github.com/yumingj/Text2Human.

15세기 중국에서 유행한 조선의 마미군 패션, 그 문화적 변용에 대하여

The shape of the skirts of Chosŏn women was a dressy and wide throughout the Chosŏn Dynasty. So, there was a device to maintain the volume of the skirt. Usually several layers of undergarments were worn, which served as support for the skirt to look fuller. In the Chosŏn Dynasty, women''s underwear not only played a sanitary role, but was also a part of fashion. At that time, women had to wear several layers of underwear under their skirts to follow the fashion of Chosŏn. A lot of fabric is needed, and these fabrics aren''t cheap either. In Cheju Island(濟州島), agriculture was not developed, making it difficult to produce textiles, so they had to go to land cities by boat to purchase them. Cheju people wanted to find a more economical way, and that was mamigun(馬尾裙). In Cheju Island, it was more economical to wear a petticoat made of horsehair than to wear multiple layers of underwear when trying to create the effect of making a skirt look dressy. On land in Chosŏn, cotton fabrics are cheaper, but in Cheju Island, it is difficult to find fabrics, but horsehair was easy to find. In the end, Cheju''s economic environment changed the materials of clothing. Mamigun was ‘the product of cultural transformation’ that followed the fashion of Chosŏn and replaced the material with horsehair, common in Cheju, instead of textiles. Mamigun, born in Cheju in the 15th century, was a unique ''petticoat'' in that an underskirt was made of horsehair. In the West(西歐), the petticoat made from horsehair, crinoline, was also popular in the 19th century. Horsehair was used as a material for petticoats for women''s dresses in both East and West. The mamigun, born in Cheju, was transmitted to the ''Jiangnan(江南)'' region of Ming through maritime trade in the 15th century, and became very popular in the the Jiangnan area of Ming dynasty. Through mamigun, it is possible to confirm the existence of ''horsehair'', which was used as a petticoat material in both East and West, as well as the new material and shape of clothes on Cheju Island, the change of culture within Chosŏn, the history of wide cultural exchange between Chosŏn and the Ming dynasty.

Identification and protection of puce unlined clothing with printed gold and colour‐painted lace

AbstractA large number of Southern Song dynasty costumes were unearthed from Huangsheng tomb in Fuzhou city, Fujian province. The unlined clothing was among the funeral objects, and made of the most common leno fabric, which was light and transparent, and the clothing was in extremely poor condition. To protect the unlined clothing, scientific evaluation and protection were carried out. Three‐dimensional microscopy was used to analyse the weave structure and X‐ray fluorescence spectroscopy (XRF) and indirect enzyme‐linked immunosorbent assay (iELISA) were used to analyse scientifically the printed gold and colour paint components of the unlined clothing. The results showed that the structure of the unlined clothing was a two‐warp twisted leno, the printed gold contained Au, Cu, Fe and other elements, the coloured paint contained Pb and Hg elements, and the binding material component of the coloured paint was type I collagen. Based on the results of the analysis, the silk fibroin consolidation method was used to improve resistance to the stress and strain caused by the needle–thread repair method, and the needle–thread repair method was then used to restore the original clothing shape. This study provides a new approach for the protection of fragile silk fabrics in the future.

A novel approach of boundary preservative apparel detection and classification of fashion images using deep learning

Visual analysis of fashion images gain much attention in the fashion industry due to its commercial and social importance. In recent years, deep learning techniques offer overwhelming progress in improving the accuracy of fine‐grained apparel segmentation with accurate bounding box prediction. The baseline pixel‐based masking techniques show excellent performance in object detection and segmentation but sometimes ignores the boundary of objects, resulting in uneven and complicated segmentation masks. Moreover, it is time taking to generate a multi‐scale feature map against each anchor box. To remedy this problem, a more accurate, faster, and suitable deep learning architecture is proposed that accurately detects, classify, and performs fine‐grained segmentation of cloth products in a single platform. In this paper, initially, an Object Class Head Detector model is proposed in which the baseline Mask‐RCNN model is used as a reference model. Here, we replace the Region Proposal Network with the proposed modified YoloV2 model to locate apparel products with its class prediction. The modified YoloV2 model has more capability to detect tiny objects because of local and high‐level feature fusion. The goal of this step is to accurately locate the objects in minimum time intervals. Furthermore, the predicted bounding box is converted to object shape offsets using deep snake architecture that tightly fits onto the apparel shape. It can improve the accuracy of cloth shape segmentation by preserving object contours. The proposed architecture is empirically validated on various existing fashion image datasets. The experimental results illustrate that the proposed architecture performs better on the Deepfashion2 dataset with mAP of 86.86%, as compared to other state‐of‐the‐art deep learning models.

Accurate simulation of draped fabric sheets with nonlinear modeling

This paper reports a numerical approach, based on a nonlinear particle spring model and a collision detection procedure, to simulate the shape of a draped cloth, or a flexible sheet, together with a simple but precise three-dimensional shape reconstruction method for real fabric applications. The latter is utilized to verify the accuracy of the proposed drape simulation model. The drapes of four types of fabric on a cylinder are simulated, and the results are compared with the reconstructed shapes of the same cloths; the results show an excellent agreement. The simulation model is further used to calculate the shapes of skirts of different materials and sizes, and the effects of fabric parameters, length, and waist size are numerically investigated. The results reveal that under the same conditions, the behaviors of different materials are affected by their properties in terms of stiffness coefficients of the springs. The silk skirt looks soft and fluttering; the outer contour curve of the skirt simulated for the polyester fabric appears relatively smoother, but the shape of the cotton skirt seems to be stiffer. The skirt made of fabric of 10% cotton and 90% polyester combines the characteristics of the polyester and cotton fabric.

Research on the Ming Dynasty Dao Robe Modeling Method Based on 3D Simulation Technology

With the rapid development of 3D virtual fitting technology, the study of virtual simulation models of traditional Chinese clothing is of great significance to the establishment of a visual database. In this article, the Ming Dynasty Dao gown is taken as the research object. First, the method of combining historical documents and archeological objects is used to analyze the Dao gown’s clothing culture, plane structure, and modeling characteristics in depth. Use virtual simulation technology to establish a three-dimensional model, and simulate and reproduce the color pattern, fabric material, human body proportion, and replica modeling of the robe. In the research of this thesis, the clothing CAD technology is used to restore the historical pattern and the three-dimensional simulation technology to show the effect of the ready-made clothing, and more concretely reproduce the clothing shape and style characteristics of the Ming Dynasty Taoist robe. The modeling method of 3D simulation technology provides new ideas for the reproduction and reproduction of visualized Chinese historical costumes and the promotion of the digital inheritance of Chinese historical costumes.