StackSpark

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Inspiration

Online fashion shopping is convenient, but one of its biggest challenges is uncertainty. When buying clothes online, users can see what a garment looks like on a model, but they cannot easily visualize how that same garment might look on themselves.

We wanted to solve this problem by making online fashion shopping more interactive and personalized.

This inspired us to build DripMatch, an AI-powered virtual try-on experience that allows users to upload a photo of themselves, select a garment, and see an AI-generated preview of themselves wearing it before deciding whether they like the outfit.

Our goal was to go beyond simply integrating an AI API. We wanted to build an actual product experience around the technology, including ways for users to save, revisit, download, and manage their try-on results.

What it does

DripMatch allows users to explore clothing and virtually try on selected garments using the YouCam Apparel Virtual Try-On API.

The user can upload a reference photo, select a garment, and start a virtual try-on. The application sends the required information to the YouCam API, waits for the AI task to complete, and displays the generated result.

After generating a result, users can:

• Save their favorite looks to the Favorites page.
• Keep results they are still considering in Drafts.
• Move a draft into Favorites later.
• Download their generated try-on images.
• Open results in a larger preview.
• Delete individual results.
• Clear their saved Favorites or Drafts.
• Switch between light and dark mode.
• Enable or disable automatic draft saving.
• Manage their stored Favorites and Drafts from Settings.

The Drafts feature is particularly useful because users do not have to make an immediate decision. If they choose to try another outfit without favoriting their current result, the result can be saved as a draft and reviewed later.

How we built it

DripMatch was built as a modern web application using React, TypeScript, and CSS, with Framer Motion used to create smooth animations and transitions throughout the interface.

The core AI functionality comes from the YouCam Apparel Virtual Try-On API.

The virtual try-on workflow involves several stages. We handle the user's image and selected garment, initiate an AI task through the API, monitor the task while it is processing, and then retrieve and display the generated result once it is ready.

We also built application state around the generated results so that users can manage their previous try-ons through Favorites and Drafts.

The interface was designed to make the AI process feel simple from the user's perspective. Instead of exposing the complexity of the API workflow, the user sees a straightforward experience: choose a photo, select an outfit, try it on, and decide what to do with the result.

Challenges we ran into

One of our biggest challenges was integrating an AI API with an asynchronous workflow.

The virtual try-on result is not generated instantly. After creating a task, the application has to wait while the AI processes the request. We therefore had to implement task-status checking and handle different states such as processing, success, and errors.

We also encountered challenges with API parameters, file handling, retrieving generated results, and making sure the application did not attempt to access a result before it was available.

Another challenge was building a useful experience around the AI result itself. We didn't want the application to simply generate an image and stop there. We had to think about what happens after the generation: What if the user likes it? What if they are unsure? What if they want to compare it later?

This led us to develop the Favorites and Drafts systems, as well as download functionality, previews, deletion controls, and confirmation dialogs.

Accomplishments that we're proud of

We are proud that we were able to turn an AI virtual try-on capability into a complete user experience rather than just an API demonstration.

One of our favorite parts of the project is the way Drafts and Favorites work together. Users can experiment with different outfits without worrying about losing previous results, while still being able to organize the looks they actually like.

We are also proud of the overall user experience. The application includes animations, responsive interactions, dark and light themes, confirmation dialogs, image previews, downloadable results, and configurable draft behavior.

Most importantly, we successfully integrated the YouCam Apparel Virtual Try-On API into a working fashion application and built the surrounding experience needed to make the technology useful to an actual user.

What we learned

This project taught us that integrating AI into an application involves much more than sending a request to an API.

We learned how to work with asynchronous AI tasks, handle task-status polling, manage generated results, and build appropriate loading and error states around an AI workflow.

We also learned more about application state management and how different features need to work together. For example, a try-on result can move from being a newly generated result, to a draft, and eventually to a favorite.

From a product perspective, we learned that the AI itself is only part of the experience. The surrounding interface, usability, data management, and user controls are equally important in turning an AI capability into a useful product.

What's next for StackSpark

The next step for us would be to take DripMatch beyond the prototype and move it closer to a production-ready fashion platform.

Some of the features we would like to explore next include:

• Expanding the clothing catalog with more garments and categories.
• Supporting more advanced virtual try-on scenarios.
• Adding user accounts and cloud-based storage for try-on history.
• Allowing users to compare multiple generated looks side by side.
• Adding product purchasing and checkout functionality.
• Providing personalized clothing recommendations based on a user's saved looks.
• Improving the speed and reliability of the AI generation workflow.
• Exploring additional AI-powered fashion features such as outfit recommendations and styling assistance.

Ultimately, we want DripMatch to evolve from a virtual try-on demo into a complete AI-powered fashion assistant that helps users discover, visualize, and confidently choose what to wear.

Built With

• framer
• react
• restapi
• typescript