Our data assets and data processing workflow are the strong foundation for implementing AI Fashion.

1. The market size and commercial value of AI graphic processing models in the fashion industry.

The global market size of the fashion industry is about $1500 billion per year, while the semiconductor industry has a global market size of approximately $500 billion per year. There is an urgent need for reshaping and tapping into the overlooked immense potential for lucrative opportunities in this untapped blue ocean.

Currently, AI graphic processing technology is widely used in various fields, including computer games, VR, AR, film production, advertising design, medical image processing, and more.

If all of the aforementioned fields involve a connection with garment, then the apparel industry is, in fact, one of the industries with the most extensive application scenarios for AI graphic processing.

However, the digitization level of the fashion industry’s core design and development process, which is the most crucial part of its value chain, lags far behind other industries.

2.  The current status and reasons for the application of AI graphic processing models in the apparel industry.

The application of AIGC in the apparel industry is currently limited to assisting in the design and creative stage.

*AIGC(AI generated content)imagines 

It has not yet empowered the crucial core aspect of this industry. This refers to rapidly converting selected design images into basic pattern graphic data that can be directly used for cutting and sewing.

Furthermore, the clothes created by cutting and sewing according to the converted output of these basic pattern graphic data must ensure that the wearing effect for ordinary consumers faithfully reproduces the effect of the original image. It should not be limited to the tall and slim body proportions of 180cm supermodels, as shown in the picture above.

Currently, people can only obtain approximate design style images through AI-generated graphics. These images do not provide detailed structural line information for each specific body part that can faithfully replicate onto a designated consumer’s body. Garment is three-dimensional and is composed of pieces that are cut, assembled, and sewn together. These individual pieces are patterns which are cut along the structural lines associated with each body part. Detailed information about the construction and shape is required not only for the front view but also for the side and back views.

To recreate the garments depicted in these images, the current process still requires a pattern maker to manually infer and determine the specific cutting and construction lines for the shape and treatment of the front, side, and back views. This is done to ensure that each individual pattern piece can be assembled to create the various forms of clothing, as shown in the picture on the left. Additionally, this process must take into account factors such as the body shape of ordinary consumers and the comfort of wearing the garments. There hasn’t been any change in this process.

  Especially for complex design, pattern makers in the apparel industry often rely on physical draping method. They use cotton grey fabric directly on a physical torso(mannequin) to attempt to recreate the desired image effect. However, this approach relies on closely fitting the fabric to the torso.

  When it comes to actual consumers wearing the garments, achieving the appropriate ease between the clothing and the body becomes a labor-intensive process of repeated adjustments to ensure comfort. It is not as streamlined as our digital design solution (as shown in the image below), which involves accessing a 3D digital torso that can optimize the specified consumer group’s body shape. Based on the digital torso, a garment prototype model is established with the necessary looseness allowance already incorporated for optimal wearing comfort. From there, the garment patterns can be directly cut and unfolded into various shapes, allowing for a one- stop acquisition of patterns that faithfully reproduce the design effect.



(PS: The hollow space between the 3D digital torso and the outer garment represents the incorporated looseness allowance.)


In fact, with the increasingly powerful AI graphics, there is a severe imbalance in work efficiency for pattern makers involved in actual production, leading to growing work pressure.


The current principle of AI graphics generation is based on the random combination of prompts. It does not involve on-demand creation based on pre-trained and standardized garment models. The logic behind such random


combinations does not allow for the simultaneous generation of 3D garment prototypes with the necessary knowledge of the structure, cutting lines, and shapes of various parts required to recreate the clothing in the image. Furthermore, it is also unable to determine the feasibility of executing such designs in reality unless a variety of garment 3D prototype models are provided to the AI beforehand.


To achieve AI automatic pattern-making, there are several prerequisites and steps involved, in addition to the well-known collection and preparation of massive style data, as well as organizing the professional knowledge of production line processes. Among them, the following are crucial:


Data annotation: The collected pattern data needs to be annotated, which involves providing key points or bounding blocks for each pattern, enabling the AI to learn the shape and structure of garments.


Model training: Utilizing the prepared annotated data, a deep learning model is trained, typically using techniques such as Convolutional Neural Networks (CNN) or Generative Adversarial Networks (GAN), to learn the ability to extract garment shapes and structures from input images.


Image processing and feature extraction: The trained model is used to process input images and extract information about the shape and structure of garments. This involves techniques such as image segmentation, key points detection, or bounding block prediction.


That is to say, in the fashion industry, there are indeed two important AI graphic processing components: AI Garment Construction, which focuses on automatically recognizing the 3D structure and cutting lines of garments, and AI Generated Content, which involves generating images using AI algorithms.

With AI Garment Construction as the core foundation, which enables the recognition of garment structure and cutting lines, the other AI Generated Content component can use the extracted garment shape and structure information to generate patterns suitable for sewing using graphic processing algorithms. Automatically obtaining patterns becomes a natural outcome.


Now, some research institutions and large companies are attempting AI-generated pattern technology, and there has been limited substantial progress despite significant financial investment.

We believe the fundamental reason for this is that the existing pattern graphic data in the field of apparel is mostly created through traditional methods, directly drawn in 2D graphic design software, and measured in centimeters (cm).

However, actual garments are three- dimensional, and the shapes and structural lines presented in their 3D constructed state are often not achievable   through   2D drawings.

The left-right asymmetric design shown in the left images involves continuous and integrated irregular cutting lines. Since these lines are determined  directly  on  a  3D digital


prototype model of the garment and unfolded in real-time, they can faithfully conform to consumers of different body types while maintaining a consistent position and presenting a unified appearance.

Attempting to achieve the same result through traditional methods in 2D graphic design software is essentially impossible.

Here’s a simple example as below to illustrate the reason: On a three-dimensional surface, triangular shapes and the positions of their vertices cannot be accurately represented or located using flat drawings. It is not possible to draw triangles with internal angles exceeding 180 degrees or precisely determine the location of vertices through flat drawings.



Therefore, using traditional 2D drawings to faithfully reproduce 3D garment models does not meet the efficiency and accuracy requirements of the data annotation process necessary for AI-powered automated pattern generation, both in terms of workflow logic and precision.



3. The data asset advantages of SSFOX (Japan) and SdibiT (China)

SdibiT’s data analysis & processing output technology and know-how originate from SSFOX, our partnership company based in Japan. Over 17 years, we chose the Chinese market as our first target for business expansion because of the fact that China, particularly in women’s fashion, has the largest consumer base in terms of sheer numbers worldwide. Starting our endeavors in China allows us to put our ideas and value proposition into practice. From the perspective of validating and improving our technological services, as well as making genuine efforts towards environmental conservation, this venture holds significant meaning and value.


In the era of AI, achieving fully automated pattern-making requires a substantial amount of high-quality data and computational resources for training and processing.

It also necessitates specialized domain knowledge to address the complex challenges of seamless integration between garment design and production line manufacturing.

This includes understanding the basic shape of garments, cutting lines, and assembly methods, among other factors.


Generation logic of garment pattern data assets for SSFOX (Japan) and SdibiT (China) follows a 3D → 2D →3D process.

Starting from pre-established 3D prototype models for various garment categories, different structural lines are directly designed on these models. Through algorithms, these lines are then instantly flattened and unfolded into 2D pattern outputs. The resulting graphic data is measured in millimeters (mm) and retains two decimal places. Only with such pattern data can the faithful reproduction of 3D garments be achieved, allowing for on-demand and accurate pattern retrieval.

Here, taking a women’s blouse as an example, as shown in the image below.

The intersection points we annotate on the 3D prototype model of the garment correspond to the same points regardless of the different shapes and patterns generated later on. In other words, the 3D digital garment prototype serves as the “DNA” of our digital design and pattern-making solution. Just like human genes, even though the final appearance may vary greatly after evolution, since it is DNA, tracing and verifying it will always lead back to the corresponding original point, as the example shown in the image below.

This is of significant importance in the data annotation phase, which is an indispensable part of the AI learning process. It ensures consistency and accuracy throughout the pattern-making process, allowing AI algorithms to learn and adapt based on the annotated data.

This technological workflow allows our garment pattern data assets to possess the following characteristics:


High Quality and Precision: The pattern graphic data is measured in millimeters with two decimal places, providing more accurate measurements and richer detail information. Training AI models with high-quality pattern graphic data enhances the accuracy and reliability of the AI-generated patterns.


Facilitates Free Exchange and Combination: Traditional 2D drawing software cannot achieve the flexibility of combining pattern pieces like our pattern graphic data does. The ability to flexibly combine pattern pieces enables the creation of different designs and styles (we call it the magic of mix&match). This flexibility and combinability, when harnessed by AI’s discriminative combination, can lead to exponential growth in disruptive design innovation.


Historically Validated and Practically Applied:The pattern graphic data has undergone over 10 years of practical sewing production validation. It can be used to cut and sew garments with different designs through free exchange and combination. This flexibility allows for quickly meeting customers’ personalized demands during the sales process. Our physical stores in Wuxi, China, have gained popularity. Through these experiences, we have developed rich and unique know-how in assessing the feasibility of pattern data in real production lines.

Scarcity and Uniqueness:Pattern graphic data that simultaneously meets the above three characteristics is scarce and unique in the market. Other companies or organizations would find it challenging to obtain or replicate similar data.

There is a growing demand in the market for automated pattern generation. However, pattern data that can accurately reproduce design concepts, particularly those with proven supply, is scarce. Our next goal is to make more industry enterprises and organizations aware that our data assets can meet these demands and be applied in relevant industries.


Business promotion strategies


We aim to cater to institutions and companies engaged in the research and development of AI automated pattern-making technology by providing a substantial amount of high-quality training data that meets their specific data requirements and conditions.

This includes optimizing digital body models for specific demographics, digital garment prototype models for various garment categories, and an extensive collection of pattern data that can be directly derived from 3D garment prototype models. This pattern data exhibits various shapes and fulfills the criteria for free exchange and combination.


Some research institutions and companies have invested heavily in developing technology for automated pattern generation. Here are some well-known institutions and companies we are aware of:

  OpenAI : Their researchers have been exploring the application of computer vision and generative models in the field of fashion design, including automated pattern generation technology.

  Adobe : A renowned creative software company, Adobe has been developing AI and computer vision-based creative tools. Their research team is investigating automated pattern generation technology to simplify the process of fashion design and manufacturing.

  Stitch Fix: A personalized fashion subscription service company, Stitch Fix utilizes machine learning and AI to recommend clothing styles to users. Their research team has been studying how to use AI technology to generate personalized patterns to better meet user needs.

  Loom.ai : A company specializing in virtual reality and augmented reality technology, Loom.ai’s researchers are exploring the use of machine learning and computer vision to automatically generate virtual garment patterns, enhancing the virtual reality experience.



(The information provided above is based on our knowledge and may not represent the most current developments in these companies’ research activities.)

【We are here!】

For the first time, we are integrating home textile products with daily clothig, utilizing end-to-end digital technology support from design to production line data conversion which can be restored 1:1, and product digital showcasing.

At the 134th China Import & Export Fair from October 31st to November 4th, we will be located at booth 9.2B05.
Our clients, who benefit from our technical services as effective users, will demonstrate on-site what “win-win” truly means.

【What is AI’s view on the issue of the AI application in directly converting clothing designs into production patterns?】

We have held the 3D digital fashion design & pattern-making workshop regularly for three years. This year, for the first time, all five participants have a master’s degree or higher, with one participant holding a Ph.D.

During the first day’s discussion, one of them brought up the idea of hearing AI’s opinion to verify the perspective and method I presented.
It seems that AI agrees with me as the attached screenshot photos❗ Ha-ha…
The text also highlights in blue that “This is a complex task that requires a deep understanding of garment construction and patternmaking principles” – which suggests that AI recognizes the importance of having a strong foundation in the principles of garment construction and patternmaking to successfully carry out the proposed approach.

In fact, even before the emergence of powerful AI drawing tools like today, we have been offering consumers a whole solution to customization haute couture as following.
Without AI drawing tools, consumers can use our relatively sophisticated models for simple customization and creation, and choose from designs to create their own unique clothing.
For more than a decade, we collected fashion design data from fashion shows published in Vogue by top brands. And these data work as AI drawing tools.
We select the design suits actual target consumers from abundant pictures and integrate various construction lines‘ essence.
And the method used throughout is to design directly on the 3D digital garment model made from the 3D digital torso and output 2D patterns in real time.


We are also proud that SSFOX/SdibiT is the only private company that has been featured by the famous economic program #WBS(“world business satellite” )of #TVTokyo as an international digital fashion pioneer.

31st May, 2023

【Will the AI refuse to accept pattern data generated in 2D CAD drawing software for further processing?】

Generative AI is a hot topic of the moment, and we all know that everything AI can do requires a process of learning from the data we provided.

Let’s start to think about AI application in clothing pattern generation, and we will realize a very serious and logically important issue.

The ultimate forms of both virtual clothing and physical clothing are 3D. And the common source is clothing pattern data.
If the pattern data provided for AI learning is generated from 2D CAD drawing software, then the source data is not generated in 3D state, it is not logical that AIGC can play a role in this situation.
All kinds of existing picture generation, can only be skin, and can not achieve the real meaning of DX❗️

2D and 3D are completely different dimensions, so no matter how AI learns, it can not make perfect transformation.
AI need to learn that a combination of 2D patterns can be generated with different shapes based on cutting lines on the same 3D garment model.

If there were no massive pattern data generated like our method to provide for AI learning, there would never have been an AI product that could directly restore and convert a picture of a garment worn on people into pattern sets that could be produced automatically or semi-automatically❗


According to the content of an interview with Professor Geoffrey Hinton at MIT Technology Review on May 3, soon AI will be able to conduct thought experiments.
Will the AI refuse to accept pattern data generated in 2D CAD drawing software for further processing❓🧐🧐

21st May,2023
#design #digital #digitalfashion #digitaltwin #3dmodeling #sustainability #apparelindustry #metaverse #fashiondesign #AIinfashion #AIGC #business

A Digital Fashion Show Workshop with Sichuan Fine Arts Institute

This is a digital fashion show completed by Sichuan Fine Arts Institute and SdibiT., Ltd. Fashion students have their own design ideas. But how to turn that idea into a feasible design and make it true? Here SdibiT plays its role. We make it come true and make it more vivid in VR display.

Digital Fashion/Digital-twin Fashion/DigitalTwin-Matching Fashion

Digital Fashion/Digital-twin Fashion/DigitalTwin-Matching Fashion, are three different concepts.
Today, there are overseas brands coming to consult: What are the highlights of your #metaversefashion video on Fashion Week❓

Our video is dedicated to achieving DigitalTwin-Matching virtual display.
It means the physical clothes that are truly delivered to consumers are not only the appearance, but also the wearing effect matches the virtual display as much as possible❗

There is still a big difference between the costumes of animation or game characters in pure visual entertainment:
1. Garment modeling
The patterns generated traditionally from 2D Cad and provided by the brand, are not directly used❗
We verified patterns by our data processing system with 3D calculation optimization and reconstruction.

2. Vmodel modeling
The characters’ body shape is also the output of our system processing, not a detached supermodel or anime game style body shape❗

Digital Fashion ❓
Digital-twin Fashion ❓
DigitalTwin-Matching Fashion ❓
Just tell SdibiT your application scenario and purpose, then you will get a one-stop arrangement.

March 29, 2023

【How the ordinary ready-to-wear business balance high level design and pattern making cost with revenue? 】


This article is specially for people who are not satisfied with changes limited in fabric colors and patterns.

The way to generate 2D patterns that addresses Fit&Size Issues is very different from the traditional way of making clothes today.

First, create a 3D prototype with a silhouette that ensures comfort.

Second, create 3D Garment Models according to different requirements from customers or different sizes. Then generate 2D patterns.

Using our company’s method, it takes more than 3 times working hour to make 2D patterns corresponding to #DigitalTwinMatching.
That is to say, it is much more difficult to make patterns achieving good Fit&Size.

At the end of last year, we accomplished a job of making garment 2D patterns which realizes DigitalTwin-Matching according to designated design drawings. This is the most difficult job so far and it charges more than $12,000 for 1 Look& 1 Size considering time and labor cost.

Brands that can bear such high pattern making cost are limited in #luxury brands like Louis Vuitton, CHANEL, Valentino, Versace, etc.

Then how the ordinary ready-to-wear business balance such high design and pattern making cost with revenue?
Learn from other manufacturing industries.
1. Maximize the use frequency of universal design:
Reuse 3D designs or 2D patterns that have already been developed, and increase production volume by using a 3D design developed once for multiple items.

2. Minimize design changes:
Minimize the parts subject to change to reduce additional costs associated with design changes.
In other words, standardize the design.
Secondly, in the fashion design section, turn the priority work from making 2D patterns to management of component design list.
At present, the traditional working mode is that the design section starts from scratch to operate according to different specifications every time. Data information is isolated and fragmented, resulting in a great waste of repetitive time and labor.
Working mode of the design section needs to shift from “centered on design /making 2D patterns” to “centered on BOM(Bill of Materials) management “.

It’s not the standardization of work.
It’s about regularizing the design.
Promote modularization while meeting the specific requirements of different customers.

Matching and combining modular design parts is our Mix&Match-Technology solution.

Here is a cost comparison list.
If the current design cost of 1 look is 100.
Adopting our solution, the cost of 1 look would be 300.
But if you reuse the design with modular management for 10 times and make 10% design fine adjustment every time, then the cost would be 300+ 30×10 =600, and 600/11 is less than 60.

Modular design of best-selling products can greatly reduce the cost and help ordinary ready-to-wear brands achieve high design level as top brands.

8th, March

【Some parts of technology logics of AI search tools are similar or even same. Can any giant company plus α to differentiate itself? Starting from AI fashion with us is promising】

After the company OpenAI, funded by Microsoft, released #ChatGPT and combined it to search engine Bing, #Google also announced its plan to integrate Bard, its self-developed AI search tool similar to ChatGPT.

For users, since platform tools have similar functions, will the platform that can provide in-depth services on this basis be more attractive? The demand of fashion design cannot be missed!

AI technologies have applied in fashion industry in many aspects. For example, natural language processing has been used in conversational commerce, computer vision has been used in smart mirrors, and to be noticed, generative model as fashion designers. Google, #Amazon, and the Chinses company #DeepBlue all released their AI fashion design application around the year 2016 and 2017.

However, why these AI fashion design tools did not been put into use widely?
Having a lot of AI generated design pictures in hand but have no efficient way to create pattern data to turn them into actual production is useless!
Most users want to actually wear them, not just enjoy the pictures, don’t you agree?

As mentioned in the previous submissions, the point to notice is that “Generative AI” learns from image data that already exists.
Our company SdibiT has millions of pattern pieces which can be matched and combined into a new style. We call it Mix&Match technology and it is a solution to AI generative fashion.

However, to maximize our data and technology application, we’d like to work with a professional AI team. We are going to figure out the algorithm of automated pattern matches together and generate as many styles as possible which all have the adequate data for production.

Is it an exciting work? Any team has the ambition to shake the fashion industry is welcomed to contact us🤝🤝