Can AI Replace Fashion Designers? The Answer Might Surprise You

Can AI Replace Fashion Designers? The Answer Might Surprise You

Today society values fashion as more than just something to wear. It is a way to express yourself and show the world who you are. So can Artificial Intelligence (AI) replace fashion designers? Some experts say it can, but we believe humans still play a role in the design process. Let’s explore this topic further in this blog post.

The role of AI in art and design

Technology that can mimic human mental processes may be frightening, especially when the issue of creativity enters the picture. For many, invention explains humankind’s growth throughout history, and this creative thinking distinguishes humans from the rest.

AI is typically viewed with skepticism by individuals in the creative areas because its overtones of complex logic appear to take away from the emotional method of conventional creatives. However, utilizing the Internet was unthinkable 100 years ago, and it has now become an extension of the creative process.

The Internet has not replaced painting, product design, or couture. Similarly, AI will be, and already is, a tool for taking human creativity to new heights and opening doors we didn’t know existed.

While AI might replace designers, it will do so for today’s designers rather than tomorrow’s. AI will evolve into a design collaborator and tool that designers may employ to satisfy the changing demands of the workplace.

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Can AI design clothes?

The short answer is yes; AI can design clothes. But how good or viable those designs are is a different question. Let’s review what has been done to the present.

AI enables machines to do human-like activities such as reading text, perceiving pictures, and making judgments. It accomplishes this through algorithms or sets of rules applied to data. For instance, if you provide numerous photographs of women wearing red dresses, it may ultimately learn to recognize them and eventually “design” them as well.

The availability of datasets such as DeepFashion has created new opportunities for the fashion sector. Not only can AI assist us in predicting the category of a clothing item, but it can also assist us in creating computer-generated pictures of similar-looking things.

Until the invention of Generative Adversarial Networks (GANs), making realistic fashion photos was a complicated process because of the enormous data in the photographs. Images are often high resolution, resulting in a large number of pixels. GANs have provided academics with a feasible technique of generating and validating all of this data.

A GAN is a prominent paradigm for unsupervised machine learning that involves the interaction of two neural networks – a generator and a discriminator. The generator’s job is to create graphics from the random noise it receives as input. The discriminator’s job is to determine if the produced pictures are authentic or fraudulent (or visually realistic and appealing, in the case of fashion).

Famous fashion houses have provided remarkable AI-driven solutions, such as the Tommy Hilfiger, IBM, and Fashion Institute of Technology collaboration Reimagine Retail, which aims to identify future industry trends and improve the design process.

Fashion designers have been investigating AI as an addition to their design process. For example, the German-based fashion platform Zalando worked together with Google in Project Muze to test what is possible in AI-driven fashion design.

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Many experts believe that 3D and AI technology will be around for various reasons, notably pinpointing specific consumer needs. The digital clothing sector is a feasible solution to waste and pollution concerns.

Without even realizing it, the game industry has also helped pave the path for fashion. Skins have been sold in games like World of Warcraft and Fortnight for over two decades. Few could have imagined that this notion would one day be applied to the typical consumer’s real-life wardrobe.

Characters in video games are more lifelike because of 3D AI technology, which includes subtleties like natural physical motions, facial expressions, and clothing that flow in the wind or against the body. 

As a result, this technology is increasingly being employed in the fashion industry to assess and replicate the genuine nature of the fabric and fit for consumers to make real-life or digital apparel. 

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So, will AI replace fashion designers?

Fashion design has a low automation risk (estimated at around 3%). This score is determined by the talents, knowledge, skills, and activities needed to complete the job, such as originality, social perceptiveness, persuasion, and finger dexterity. The more these a profession demands, the less likely it will be replaced.

However, there’s no doubt that AI and machine learning-driven innovation have become critical success factors for fashion design organizations in the twenty-first century.

As a result, tremendous progress has recently been achieved in adopting AI and machine learning approaches for augmented and customized design. Examples include style matching, trend predicting, interactive search, style advice, virtually putting on garments, and clothing type and style classification. 

Many people feel that AI may be useful for fashion designers, just as architects utilize computer modeling to help with the design, mainly due to AI’s ability to analyze and store massive amounts of data.

But while AI excels at assessing hundreds of fashion concepts at once, extracting the best characteristics from each, and swiftly developing a mock-up design, we believe the algorithm cannot wholly replace people.

We have our creativity, our emotions, and our human experiences. So, when paired with AI-powered creativity, we believe the future is exciting. We can collaborate with AI rather than compete with it.

We should not wonder, “Will AI replace fashion designers?” The right question is, “How will AI assist and improve fashion designers’ work?” Designers in the fashion business will adopt AI to enhance their design talents and improve their product development approach.