Personalisation-meets-VTO

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  sopping page

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  lets select this for VTO

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  original image

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  resultant try on

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  pov engine personalisation

Inspiration

Online fashion shopping gives us thousands of choices, but one important question often remains unanswered: “Will this actually look and feel right on me?”

That question inspired POV Fit Engine.

I wanted to move beyond traditional recommendation systems that simply say “You may also like this.” Our vision was to create a more personal shopping experience—one that understands a shopper's preferences and helps them make decisions with greater confidence.

That led us to a simple idea:

Don't just add it to your cart. Fit it with POV.

POV Fit Engine combines personalized fashion discovery with Virtual Try-On, allowing shoppers to move from “I like this” to “I can see myself wearing this.”

What We Built

POV Fit Engine creates a personalized fashion experience around the individual rather than treating every shopper the same.

A user can discover fashion items through the POV experience and, instead of immediately adding an item to their cart, choose “Fit with POV.” The experience then uses YouCam's Apparel Virtual Try-On API to visualize the selected clothing on the user.

This creates a simple loop:

Discover → Personalize → Fit → Decide → Buy

The goal is not to replace the shopper's judgment, but to give them better information at the moment they are deciding.

How We Built It

We designed POV Fit Engine as a web-based experience connecting our personalization layer with YouCam's AI infrastructure.

The core experience consists of:

• A personalized fashion discovery interface
• A Fit with POV interaction alongside the normal shopping flow
• YouCam Apparel Virtual Try-On for visualizing clothing on the user
• A recommendation/personalization layer that represents the shopper's preferences
• A seamless path from discovery and try-on to purchasing

The most important design decision was making Virtual Try-On part of the decision-making journey, rather than presenting it as a separate feature.

What We Learned

One of our biggest lessons was that personalization is more valuable when it happens at the right moment.

A recommendation can tell someone what they might like, but visualization can help answer a different question: “Can I see myself wearing it?”

We also learned that a successful AI product is not necessarily about putting AI everywhere. The technology should appear where it removes uncertainty or makes an interaction meaningfully better.

For POV Fit Engine, that moment is the transition between liking an item and deciding whether to buy it.

Challenges

One challenge was designing an experience that felt like a real product rather than simply wrapping an API.

It would have been easy to create a catalog, add a Virtual Try-On button, and stop there. Instead, we wanted the YouCam capability to have a clear purpose within the larger experience.

Another challenge was balancing personalization with simplicity. A sophisticated recommendation system can collect many signals, but the user should not have to understand the underlying AI to benefit from it.

We therefore focused on making the experience intuitive: find something you like, fit it with POV, see yourself in it, and decide.

Why POV?

Fashion is inherently personal. The same outfit can produce completely different reactions depending on the person, their style, preferences, context, and confidence.

POV Fit Engine is built around that idea.

We don't want online shopping to feel like choosing from an endless catalog. We want it to feel more like having a personal fitting room that understands your point of view.

And with YouCam Virtual Try-On, we can bring that fitting-room experience directly into the digital shopping journey.

Built With

• flask
• lightgbm
• ml
• sqlalchemy