Key Insights: Built a proprietary virtual try-on system with custom datasets and CGAN architectures, reducing costly returns in an industry where they can average 30% of the purchase price.

About the Client

Doris, a technology company focused on fashion ecommerce, helping retailers enhance the online shopping experience by enabling customers to see how clothing will look on them before making a purchase.

The Challenge

The client wanted to create a scalable, high-quality virtual try-on solution to support more users and an expanding range of garment types.

They faced key hurdles:

• High-resolution output was essential, but most public research and models worked with much smaller images, while higher-resolution models often had non-commercial licenses.
  
  
• Existing virtual try-on datasets were restrictive, lacked diversity, and were skewed toward homogeneous demographics.
  
  
• The need to train GAN networks on a dataset large and varied enough to handle different clothing types and body profiles.
  

Marvik’s Approach

We built a proprietary computer vision pipeline where users can upload a photo to an ecommerce site and instantly see themselves wearing the selected garment.

Our approach included:

• Designing and curating a custom, diverse dataset to address license restrictions and demographic bias.
  
  
• Developing a pipeline of Conditional Generative Adversarial Networks (CGANs) working together to achieve realistic garment replacement.
  
  
• Ensuring scalability to handle increased user volume and new clothing categories.
  

The system was built with Python, TensorFlow, CGANs, and AWS, achieving results that surpassed state-of-the-art models in the field.

The Results & Impact

• Enabled realistic, high-resolution virtual try-on directly in the ecommerce experience.
  
  
• Reduced potential for costly returns, in an industry where return rates average 20–30% and returns can cost 30% of the purchase price.
  
  
• Gave the client full ownership of a scalable, license-compliant solution tailored to their market.
  

Why This Matters

By combining cutting-edge computer vision with a purpose-built dataset, this project shows how AI can solve industry-specific pain points, transforming fashion ecommerce from a guess-and-return cycle into a confident, engaging buying experience.