Author: He Tao

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.
(https://lnkd.in/gywZcCh8)

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🤝🤝

4th.Mar,2023

As mentioned in the last submission, the consistency of data is fundamental for DX in any industry, and the fashion industry will be no exception❗
Should these enterprises integrate our scheme into the system for comprehensive application❓
Since it will truly innovate and benefit the design department of fashion industry and provide the industry with the true DX process❗

In China, our company is invited by the design school of university in the video below to hold workshops regularly. Let the teachers and students understand the basic digital design thinking method and process of design data processing by creating their own digital garments together.

The purpose of this workshop in the video is to experience the whole DX process. Therefore, left behind our company’s requirement of DigitalTwin-Matching temporarily, teachers and students can first enjoy the creation freely.

Note the first twenty seconds of the video. The method is obviously wrong if considering the actual wearer.
The rigor of phygital demands you not to draw the curve arbitrarily. How to think and deal with it, is the teaching content of our next workshop.

However, the great players of the future have realized the significance of designing directly on the “3D Digital DRAPING” according to their own idea and obtaining digital pattens which can restore the design in real time.
One small step today, one big leap tomorrow. The future is in sight👍

27th.Feb,2023

Clients who get to understand our solution for the first time often have the following response. “Ah, comparing #CLO, #Browzwear, #Optitex, #Style3D, etc, your DX solution is in the reverse order.” And I would say every time that we did not deliberately reverse, our solution just correct the process to the right track❗️

Let’s think about it together. The clothing making process 150 years ago, is just like the modern haute couture customization process. Each piece is made based on the customer’s body and confirm the right fit&size, then the 3D prototype obtained on the real body is converted into drawings of 2D pattern, being sewed finally.
In the era of industrial high-speed mass production, the process gradually deviated from the original route due to time, efficiency and cost, and the most important reason, there was no 3D digital design tools.

Strictly speaking, from the perspective of other industries such as architecture, shipbuilding, etc., #design drawings and 2D processing drawings are different things❗️

2D pattern maker’s job is not creating design drawings but generating CAM data for execution of processing output.
The popularity of 2D pattern making software in the era of mass production is because it enabled efficient management of the CAM data used in production line processing.
With the development of #digital technology, high efficiency, precision and low cost can be achieved simultaneously while the steps are corrected back into the logically right order.

Let’s think further more together. If one customer likes the design draft generated by AI and decides to buy, the seller just need to make the physical clothing faithfully restore the design while ensuring the right fit&size of the customer.
The core advantage of our solution is the ability to quickly convert designs into production pattern and achieving DigitalTwin-Matching at the same time. After this, the job can be done easily. Just sew the specified fabric according to the pattern.

So here comes the problem. At present, the pattern generated by 2Dcad is first used for 3D modeling and then be sent to the customer for confirmation. If we receive modification requirement, we have to get back to 2D pattern to do modifications. Except for some simple basic styles, will this kind of workflow definitely put the 2D pattern makers into hysteria❓
The workload do not decrease but increase because they have to use the modeling and rendering software to make 3D views of patterns they made and send to customers for confirmation repeatedly. What’s more, this kind of display only shows the appearance of the garment, it does not show the Fit&Size state at the same time…
(to be continued…)

27th.Feb,2023

  

The special TV series about underwater archaeology broadcasted yesterday is very interesting. By introducing the latest 3D simulation and modeling tools, archaeologists can make the most effective excavation plan and make records with millimetre accuracy while easing their physical workload. In this way, they preserve human historical heritage.

One doctor of the archaeologists emphasizes, the excavation work is also a kind of destructive activity in a sense. The original feature of the heritage maintained during the long period of more than 2000 years must be accurately and faithfully recorded in the state of 3D models. The heritage of mankind should also be in form of digital asset that can be faithfully CG-restored and handed over to descendant.

The stones of those hills may be used to polish gems.
If there is no millimeter-accurate 3D model to record the data in advance, how can we accurately restore the original spatial position, angle and distance of the archaeological site by memory and feeling once it has been touched?
In the same way, if the editing and modification of clothing pattern has no identification of the design position and shape in 3D state in advance, and no corresponding operation, how can we achieve the faithful 3D restoration effect by directly making the pattern in 2DCad software by virtue of memory and feeling?
 
While it has become common sense for other industries to use 3D models as the starting point for process, the fashion industry must keep up with the times.
 The change of concept is the first priority!

The benchmark for the value evaluation of pattern data assets in the future will become whether they come from digital 3D garment models instead of 2D CAD software.

24th Feb,2023

“If AI generates a large number of fashion design drawings just like the hot topic chatGPT does, what will SdibiT, ltd do to use these drawings?” A customer who visited us today asked a question.

This question reminds me of what Michio Tomii, CTO of our company, has been saying for nearly ten years, “In the fashion industry, designers who only know how to draw a graphic design will no longer be needed.”
However, his inference ten years ago cannot be proved obviously and get understanding and recognition of people in the industry because the tool which can turn the intangible data into tangible and visual information has not emerged.

Since last year, tools like “AI Art Generator” (txt to Image: DALL-E, Midjourney, Stable Diffusion… etc.) are starting to emerge, making it easy for anyone to generate stylish design images. And this kind of technology will evolve in the future.

Currently, AI can generate Virtual Show-On, but cannot output 2D patterns that can be reconstructed to the 3D-image.
To achieve DigitalTwin-Matching, highly accurate pattern data in accordance with Fit&Size is necessary.
There is no denying that AI generates design&pattern at the same time may be possible in the near future.
The point to notice is that “Generative AI” learns from image data that already exists.
 
Our company SdibiT’s approach is illustrated as the chart.
Starting from module design, we call this Mix&Match→Combination→Compilation.
By editing small pieces of patterns generated by multiple designs as parts, large amounts of pattern data can be generated.
In fact, as a complete form Phygital after integration, only a very small number of it is made public, most of it is kept internally.
That is to say, we have a considerable amount of data assets in our company, which are raw data that cannot be utilized by other companies through Generative AI.
Among the massive design images generated by AI, there may be actual clothing that is not acceptable to consumers or the actual situation cannot be completed exactly the same as the picture, the next candidate design will be generated by the “average consumer”. Based on this, multiple different candidate looks are generated while creating a design data that achieves DigitalTwin-Matching.
The “average consumer” can choose among these candidates.
This process leads to true humanity and individualization.

“In the near future, everyone will not only become a fashion designer, but also be able to confirm the effect in 3D. Fashion designers who only know how to do 2D painting will almost disappear sooner or later.” Mr. Tomii always said.

21st Feb,2023

Breakthrough、Innovation or subversion means not using the usual methods nor stick to the routines.
 
To break away from the routine, we need to think in a new way and update our knowledge.
 
The new challenge is same to everyone. So there is no ability gap between students and teachers. Everyone needs to learn new theory, principles and practice a lot. Whoever is ahead or better in the new competition will be the teacher.
 
When there is breakthrough in one industry, the past experience does not necessarily mean advantage. In the following cases, previous experience may even become a hindrance:
1. The common sense of “impossible things” judged by experience when facing bottlenecks needs to be broken. Such “impossible things” can be done at least in part or completely under specific conditions now.
2. When all practitioners are required to renew their thinking and judge by themselves “what can be done now”, only those who have passed the thought transformation can get “new common sense”.
 
In other words, only after the system update of the technical logic working mode, can we determine whether the past experience has the value of supporting the new mode.
 
Therefore, education is crucial in the process of digital transformation in all industries. Staff certainly need to be re-educated. It is more important to give new systematic guidance to students.

In previous articles we have stated that no matter the future clothing display orientation is AR、VR、MR…if adopting SdibiT’s solution, the basic data input to the display end is the same. And SdibiT can provide such data for customers to use according to their own optional display method.

That is to say, no matter which direction the industry will go in the future, we have made basic preparation to provide corresponding data.

In technology competition, time is the most expensive cost. Anyone who is willing to adopt our solution can make up the shortage and be prepared to participate in any business model together within the shortest time.

We have carried out joint education courses in the top three design colleges in China, such as Jiangnan University and Sichuan Fine Arts Institute.

SdibiT holds the willing to cooperate with fashion professional education institutions in more countries and facilitate the truly sustainable development of apparel industry worldwide.

30th Oct,2022

#digitalfashion #digitaltwin #fashiondesign #fashioneducation #artschool

We have explained in the chapter of the sustainable fashion business model in #Metaverse about 4 conditions that the output data must meet to realize the true virtual and reality interaction business mode of apparel industry.
1. Suitable methods of Vhuman and 3D Digital Torso establishment
2. 3D digital garment model establishment based on above 1, and output of the 2D production pattern converted from setting cutting lines on the 3D digital garment model
3. Interchangeable application based on modular design and repurposed design management
4. Integration of digital fabric and 3D-design

Although the software tools involved in the above four conditions have various problems, SdibiT can make the output data meet the above 4 conditions as this is proved by more than 10 years’ practice and technical logic.
This means that no matter the future clothing display orientation is AR、VR、MR…if adopting SdibiT’s solution, the basic data input to the display end is the same. And SdibiT can provide such data for customers to use according to their own optional display method.
That is to say, no matter which direction the industry will go in the future, we have made basic preparation to provide corresponding data.

Here we compare the competition of the apparel industry, which is about to soar, to a new event of the Olympics. The event participant must meet 4 conditions. In each single item competition, SdibiT may not be the top one, but SdibiT is the only one who meet all 4 conditions currently, so we will always be the champion of this new event until there is another contestant who meets all 4 conditions too.

SdibiT holds the willing to facilitate the truly sustainable development of apparel industry worldwide and be a respected environment friendly enterprise. Therefore, we’d like to align with others to participate in the team competition. In technology competition, time is the most expensive cost. Any part who is willing to adopt our solution can make up the shortage and be prepared to participate in the event together within the shortest time, jointly carry forward this event worldwide.

Two days ago, a friend introduced me to a young investor from China who was listed as one of the 2018 Forbes U30 Excellent Investors. He mainly invested in China’s semiconductor projects. Although he is not familiar about fashion industry, he raised doubts about us with his rich investment experience. He took examples of #Anta, #Alibaba’s rhino platform, etc. 

These giant enterprises have thousands of stores online and offline and accept numerous customers every day. Their daily customer number can be ten years’ accumulation of some foreign brands.

And scanning technology is quite common now. Besides human body scanner, sellers can also use iPhone to scan customers to get their 3D avatar.

So it is easy to get enough human body big data.

In terms of the scan and modeling quality, there are technologies using to build cars, vessels and even smart cities, so the data and modeling quality can be ensured.

From the point of Vhuman and #Fit&Size alone, how can only small enterprises like SdibiT has the data that meet the necessary requirements for fashion virtual and reality interaction?

The giant enterprises have money, talents and customer data. Why do they not do it? Maybe they think it cannot bring expected benefits or they are already doing pretty well, such investment is needless.

 

I keep the words that the technology department of #Alibaba’s rhino platform had contacted us. Their obstacle is not #digital flexible production, but still is the digitization of design and pattern making.

As far as I know, to serve one brand, at least 2.8 pattern makers should be deployed. If 1000 brands on the platform require the same service, does Alibaba need to gather half of China’s pattern makers to work in the building?

 

Thanks to his frankness. The conversation makes me realize that the digital technology and knowhow involved in the process of fashion design and pattern making are not easier than shipbuilding, car building or modeling in smart cities.

 

Because the 3D virtual modeling of any other industry only needs to capture and simulate the physical object. No extra process is needed after virtual modeling.

 

For apparel digital design and pattern making, digital modeling of real human body is just the first step.

 

It is impossible to draw garment cutting lines directly on roughness 3D digital avatar.

It needs secondary processing with sense of aesthetics and proportion optimization to become a 3D Digital Torso, at least.

And then, we can start to draw #design cutting lines directly on 3D digital garment model created from the 3D Digital Torso.

The traditional apparel industry, through digital transformation, will be reborn into a very young and dynamic technology intensive industry.

 

Being belittled as a sunset industry is just because the short-sighted sunset thinking and the sunset apparel enterprises like boiled frogs in warm water are ubiquitous in reality.

 

Here, I’d like to pay a tribute to all the fashion digitization pioneers!

 

16th Oct, 2022

We have stated many times that if we simply replace the catalog pictures of real models wearing real clothes with virtual avatar wearing virtual clothes, we just still only realize Imagine-Matching(virtual show-on, not virtual try-on ) .
Such simple replacement has not changed fundamentally for consumers.
In this way, consumers cannot be convinced that they can achieve the same Fit&Size effect as the virtual display when they are wearing the same clothes. Therefore, this method cannot achieve the real DigitalTwin-Matching.

As we emphasized, the key difference between real DigitalTwin-Matching and plain Imagine-Matching is whether the virtual display and physical fitting-on can achieve the same Fit&Size.

As we mentioned earlier, there are some regrettable problems with the tools used to solve the Fit&Size issue. At present, the software and hardware R&D of human body 3D modeling rarely thinks about the actual supply chain process of the apparel industry and cannot provide effective tools directly.

As shown in the pictures, importing the 3D scanning obj. file of the same person into our processing system through different software, you will see the various size value differences, height of feature line differences, positions of landmark differences, etc.

This means the traditional method of using 2D CAD software to generate patterns with size value as the standard cannot achieve Digital Twin, because in the technical logic, the final physical product can not fit the consumers well.

It even means that even if #IEEE and other standard setting organizations set the landmarks which scientifically define the BODY measurement points, enterprises adopting 2D grading mode cannot produce the clothes that meet the standards.
The measuring point of the clothes is impossible to match the corresponding position on the body surface.

Therefore, SdibiT believes that adopting the solution of establishing 3D Digital Garment Model in advance is the right way to achieve DigitalTwin-Matching of virtual display.
(details can be referred by: https://lnkd.in/gjsp2Kpg)

Unless the stability of fit is solved, the size problem will persist.
To reduce returns caused by size mismatch, the fit problem must be solved at the same time.

Isn’t it logically?

9th Oct.2022

Needless to say, people have clearly realized the universal application of AI and the far-reaching impact that AI is having and will have in various industries.
 
We believe that in the field of sustainable fashion industry, before reaching the true AI fashion, there must be a process of AI in fashion.
 
In SdibiT’s 15 years of work experience, we encountered various problems. Among these problems, we think there is one need to be solved with the help of AI very urgent and finally achieve complete automation.
 
We have explained that we already have countless body-block corrected patterns. Match and exchange them like LEGO, more than 10 billion design types can be generated in seconds.

However, at present, the number and type of matches are calculated manually. The operation efficiency is especially low when some patterns can not simply be matched and should be used with specific restrictions. And the conclusion is that, there must have judging terms first to judge whether small patterns can be matched and combined. Then the match and combination operation can continue according to match terms.
 
We are always trying hard, but we have reached the limit relying on manual calculation.
As shown in the photo, we conducted an experiment with reference to a style structure of #Burberry Prorsum RTW-F2015. You can easily find out that this process is a typical donkey work.

 
We hope that when judgment terms of feasible match are confirmed, we can complete all types of small patterns that can be matched with the help of AI. And all these combined patterns can output the 1:1 actual size patterns for production downstream.
 
We believe that solving such key problems needs the help of AI, which is the process of AI in fashion. Once this process is completed, AI automatically generates and recommends the clothes according to various terms. The recommended clothes can be produced in reality and can be directly worn by people while ensuring the same Fit&Size in real. Then AI Fashion will naturally be achieved.

CTO Mr. Tomii recommended me to read an article before. The article says that “at both brand and industry level, it’s time to leave the 3D testing laboratory and move into full production when it comes to digital product creation.” I absolutely agree with it.

We mentioned above that if judgment terms of feasible match are set, with the help of AI, match and combination of small piece patterns can be automatically completed. And all these combined patterns can output the 1:1 actual size patterns for production downstream. Then the physical clothes has the same Fit&Size effect as the virtual clothes. It can be called DigitalTwin-Matching clothes that consumers really want and can wear directly and frequently. Then the realization of AI Fashion just needs some time.

When Virtual fitting gradually becomes a common shopping method, to provide consumers with more VR clothes to choose, the design and pattern-making department will have to do a lot of work on garment simulation and visualization. Pressure of designing new looks will also increase. This means that compared with other process, if design and pattern-making department can not increase their work efficiency dramatically, it will become the cost center in the business chain.

CEO(CXO,Business Executives,etc.)must be sensitive to business cost and work efficiency. From the view of CEO(CXO), for classic style items such as suits and shirts etc., the silhouette is basically stable although the cutting lines may differ. The national costumes such as China’s #cheongsam, India’s #PunjabiSuits and Vietnam’s #AoDai etc. have their own unique cutting lines, but the silhouette of each kind basically keeps stable. SdibiT has mastered the method of digital modular design management and has the interchangeable application with countless body-block corrected patterns at the same time. Now that we have met the two necessary conditions, if we can use the power of AI to recommend new looks accurately and efficiently according to consumers’ expectations through LEGO type matches and combinations, and ensure the same Fit&Size effect between physical clothes and digital show, what a tremendous AI fashion business we can achieve.

Our CTO often said in a half-joking, half-serious way that life wear in the concept of #UNIQLO is a typical classic style category in ready-to-wear. UNIQLO spends a lot of money on collaborations with different artists every time. If president YANAI can invest 1/10 of each collaboration’s cost in the research and development of the process we mentioned above, a complete different experience can be brought to consumers.

We clearly know that the Mix&Match R&D under the AI in Fashion mode is SdibiT’s first priority at present. If there are investors who recognize our business judgement or relevant R&D teams, please contact us without hesitate. SdibiT looks forward to achieving the goal together with you and witnessing the unparalleled new experience and satisfaction brought to consumers by AI Fashion.

We encountered software and hardware obstacles during the visualization process of the invisible data processing more than a decade ago. Under the judgement standard of “seeing is believing”, it was difficult for us to get recognition from enterprises before and it also took us a lot of time and energy to communicate and present. In recent years, thanks to the progress of various visualization tools, we obviously see that the number of enterprises that actively consult and start cooperation is increasing every year.

There is one issues we must point out here. Through all kinds of attempts over a long period of time, we found that 2D grading method is the main factor that hinders #DigitalTwin-Matching，and it can not meet the data requirement of #AIinFashion based on algorithm. Each size must be set in 3D version by 3D grading method in advance and then be converted to 2D patterns. In this way, small pieces patterns are possible to automatically match and combine with AI.

3D→2D→3D, the source must be the design in 3D state. Only in this way, the generated 2D pattern used for production and #3Dmodeling can restore the original 3D design on 3D human body. This step is irreversible.

In the 2D→3D way, if you do 3D modeling using the patterns generated in 2D Cad by 2D grading, automatically generating different sizes patterns according to a certain ratio, you can only get Imagine-Matching. You can not ensure the consistent fit&size of each size no matter how the design changes. On the other hand, even if the side lengthes or perimeters of the patterns of different sizes are the same, in order to ensure fit&size on the body, the shape of the 2D pattern must be modified according to different parts of the body, it means that it must be the pattern corrected by BodyBlock, otherwise, it can’t meet the accurate judgment terms using AI.

As one of our experimental cases shown in the picture, referred by #LouisVuitton‘s structural line, the shape of this small piece is different in each size by 3D grading after BodyBlock correction, that is impossible for 2D grading.

If we continue to use 2D grading method, it means that we choose to give up using AI, a powerful tool, and take another path.

In fact , we have figured out the working efficiency difference though our working experience statistics. Using 3D grading, once the interchangeable application based on #digital modular design management is formed, the average operation time of design and pattern-making is less than 1/10 of the traditional way, and even can be 1/32 when doing works for regular category.

That is to say, using the same method as SdibiT and doing 2-3 years #design data accumulation and management work, annual output of about 20000 looks like #ZARA will no longer be the benchmark of the industry, but the norm.

If there are investors who recognize our business judgement or relevant R&D teams, please contact us without hesitate. SdibiT looks forward to achieving the goal together with you and witnessing the unparalleled new experience and satisfaction brought to consumers by AI Fashion.

In short, as shown in the picture, this is one of the cases we have experimented repeatedly.
We generated body-block corrected patterns with reference to the construction lines of #Versace’s dress. Guests of different sizes can freely choose the parts of sleeves and skirts and do personalized combination to small pieces of patterns. Our designers and pattern makers do not need to do extra work. They only need to retrieve the pattern pieces from the style database according to customers’ requirements and output the final complete pattern. Then the factory can produce and deliver the same goods expected by end consumers.

In the next 3-5 years, we expect to improve the functions step by step, cooperate with excellent teams in the world, and gradually form a shopping mode connecting metaverse as the heroine X we discussed before.

At present, there is no other solution we can think of that is more environmental friendly.

11th Sep, 2022

We have explained our ideas and practices on sustainable fashion business model in #metaverse which is far more grand and diverse than the real world. Next, we would like to add a supplement opinion. What preconditions, preparations and integration methods are necessary to realize such a business model?
 
First of all, we clearly understand that to realize sustainable fashion in the physical world, the root solution is avoiding producing physical waste in every link. And the essential method is producing according to sales through #design and pattern making management.
 
Because metaverse breaks all kinds of physical boundaries and restrictions in real life, people can freely switch the clothes that only exist in the virtual world or the clothes that can be worn both in the real world and virtual world. Then, the part that may generate waste is the sales of physical clothes that also exist in metaverse.
 
Therefore, to avoid return and even disuse of clothes because of the big gap between the physical ones and virtual appearances, the technology that can be integrated into the metaverse is not simple imagine matching. It must be Digital-Twin Matching which can also confirm fit & size of the clothes.

For example, as for the well-known automatic driving and smart city, the integration of any industry with the metaverse will adopt the most appropriate integration mode according to the respective characteristics and target scenarios of the industry. However, no matter VR, AR or MR is used, it must undergo reality simulation, virtual modeling and digital management. For fashion industry, there is no exception.
 
Physical→PHGITAL→Digital, These are necessary routes.
1. Physical Digital fashion, the digital fashion which informs the way we produce physical fashion. The product therefore is physical.
2. Phygital fashion, physical and digital combined. The product is digital fashion that can be worn by humans.
3. Fully Digital fashion, sold direct to avatar.

Over the past 15 years, SdibiT has been aiming for the Phygital fashion, mastered the full methodology and know-how of the integration process.
1.Vhuman database establishment;
2. 3D clothing digital design pattern database based on Vhuman;
3. Interchangeable application based on digital modular management;
4. The integration of digital fabrics and designs has also been tested.
The preparation work has already been finished. Just like Bingo Game, we have arrived at the point waiting for the fifth one.

In the video, both images are about changing clothes, one is dazzling, the other is plain.
However, the essential difference is that SdibiT can ensure that whether the end consumer wear the clothes on his 3DAvatar or physical body, the general looking and fit&size effect are the same.

In fact, with the data and technology accumulation held by SdibiT, millions of different sets of clothing that can be worn in both virtual and real world can be generated. Limited by current platform(software&hardware), it cannot be visualized completely and adequately(to be explained later).

Now, we talk about fabric, one of the three major elements of clothing as people, design&pattern.

At present, the reality is that from the perspective of color and graphic, most fabric manufacturers rely on fashion prediction to develop and produce products. Ready-to-wear fashion designers only can do designs using fabric samples sent from fabric manufacturers, also relying on prediction of consumer demand. Following these steps, designers can not completely understand and respond to the real demands of end consumers. Each country, region and brand will also have different information because of their own policies. We believe that the waste and environmental problems caused by repeated procurement, repeated R&D and prediction errors need to be solved as soon as possible from the perspective of raw material resource allocation of fabrics. As mentioned before, if the Eco-friendly fabric with hard-working of R&D are not sold and turned into inventory, finally can only be discarded, is it contrary to the original intention of environmental protection?
Apart from the fabrics that must be dyed first in the process, we will talk about the solution for fabrics which can be printed or dyed with colors and graphics after weaving. And this solution can be put into use immediately.

Imagine the following scenario. The original white textiles before printing and dyeing made from materials bio or marine-degradable are allocated reasonably at cooperative factories around the world. After receiving the order from the end consumer, the system recognizes the delivery address, appoints the nearest factory that can print and produce according to consumer’s requirements to finish the order. Thus, the delivery logistic is the shortest distance. 

As shown in the picture, SdibiT has completed the integration test of #digital fabric and design. Although the number is small, we insist in involving consumers into #design process and delivering goods in this way. We are looking forward to cooperate with more partners to deepen our practice in the real world while exploring the feasible solution in #metaverse.

While the Metaverse brings new production and consumption processes, it will also cause changes in perceptions of reality and demands. Therefore,  it is expected that the sustainable development of the textile industry will be accelerated.

2nd Sep,2022