RealFit

RealFit was inspired by our shared frustration with online clothing returns. Despite the convenience of online shopping, sizing inconsistency often leads to disappointment, wasted time, and unnecessary returns. After repeatedly ordering clothes that looked right on a model but fit poorly in reality, we wanted to create a solution that gives shoppers confidence before purchasing. This motivated us to build RealFit—a system that generates an accurate 3D rendering of a person using a simple 360° video and applies real clothing from real brands to show how apparel would fit in real time. To build RealFit, we took inspiration from TailorNet, particularly its approach to body modeling and garment deformation. Our system reconstructs a user’s true-to-scale 3D body by extracting silhouettes from the uploaded video and fitting an SMPL-based model that captures individual measurements with high precision. Once the user’s avatar is created, they can upload clothing items, brand-specific files, or standardized garment templates. RealFit then deforms these garments to match the user’s exact dimensions, focusing entirely on accurate fit visualization rather than full physics simulation, allowing for faster and more reliable results. Throughout development, we faced a number of challenges, especially with dependency conflicts, environment setup issues, and frequent UI rendering bugs. TailorNet’s lack of a direct Python install required us to restructure parts of the library manually, and integrating our model pipeline with the frontend often broke due to mismatched versions or rendering glitches. However, working through these problems taught us a great deal about debugging complex ML pipelines, managing Python environments, and designing stable user interfaces. In the end, RealFit demonstrates the power of combining 3D modeling, machine learning, and practical design to solve a real problem that affects millions of online shoppers.

Built With

• css
• javascript
• python
• vscode