AI for trying on clothes - why are there still no good products?
Ilya Berdysh
9 replies
It's a very cool and understandable need - it's hard to choose clothes. Many people use stylists, but not everyone has access to them. The rest use Pinterest or influencers on instagram.
A single window for fitting could be the first point of contact with the user. Customize for you, choose the right clothes, go to a partner to purchase
Why doesn't this exist yet?
Replies
Clair Birge@clair_birge
You're right, an AI for trying on clothes seems like a much-needed tool in today's digital shopping era. The technology is certainly evolving, but it's complex to create a universally accurate system. Factors like body measurements, fabric behavior, and personal style preferences make it challenging. Some companies are experimenting with virtual fitting rooms and AR, but they're still perfecting the accuracy. I think this would be a very useful innovation for luxury brands such as Chanel, Dior, Gucci, etc. because their stuff is too expensive to order without trying it on. So I don't complicate my life and order such clothes from 레플리카 1위. There are always things of high quality and at very favorable prices, so I recommend this store to everyone.
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Visualizee.ai
I am convinced that there will be soon. AI models just need to improve a little bit, but the community is doing an excellent job in that area.
@pobidowski thanks for answering! In what part of the technical process do you expect AI to breakthrough? What is the difficulty here now?
Visualizee.ai
@ilyaberdysh accuracy and realism, but I can see that those issues are being already addressed by the community
@pobidowski I want to join them! Can you share threads or links where some active members are solving that?
Visualizee.ai
@ilyaberdysh look for stable diffusion and the whole ecosystem at github.
The development of AI for trying on clothes can be a complex challenge due to several factors:
Accuracy: Achieving accurate and realistic virtual try-on experiences requires advanced computer vision algorithms to accurately map the clothing onto the user's body and simulate how it would fit and drape. Ensuring precise and realistic representations of various body types, fabric textures, and movement can be technically demanding.
Data and Training: Creating a robust AI system for virtual try-on requires extensive training data, including diverse clothing items, body shapes, and sizes. Collecting and curating such data can be time-consuming and resource-intensive.
Variability in Fit: Clothing fit can vary significantly between brands, styles, and individual body shapes. Capturing and representing these nuances accurately in a virtual try-on system can be challenging and may require extensive customization or integration with clothing brands' specific sizing models.
User Experience: Providing a seamless and intuitive user experience is crucial for virtual try-on products to gain widespread adoption. Factors such as ease of use, real-time responsiveness, and realistic visualization play a significant role in delivering a satisfying user experience.
While there have been advancements in AI-based virtual try-on technologies, achieving a universally "good" product that satisfies all users' expectations and preferences remains a work in progress. However, with ongoing research and development, we can expect further improvements and more innovative solutions in the future.
@tori_better this answer from gpt I already know))) but there is no specificity here in describing technical or business obstacles
The development of AI for virtual clothing try-ons has faced significant challenges, resulting in a lack of highly effective products on the market. Despite advancements in AI and augmented reality (AR), achieving a realistic and accurate virtual try-on experience of lucky me I see Ghosts Hoodie is complex. This complexity arises from the need to account for diverse body types, clothing materials, and fit preferences, which require sophisticated algorithms and extensive data. Moreover, integrating these technologies seamlessly into user-friendly interfaces that can operate efficiently on various devices adds another layer of difficulty. Privacy concerns and the necessity for high-quality 3D modeling also hinder progress. As a result, while the technology holds great promise, current solutions often fall short of consumer expectations for accuracy and ease of use.