Blog #3: High Precision, High Hurdles - Lessons from my virtual fitting room build

In this final post I reflect on the journey of my virtual fitting room project. Despite the considerable challenges encountered during the full application development, mainly due to the complexity of the real-time cloth physics, the research successfully validated a powerful conceptual open source pipeline.

The Gold Standard for Accuracy A key part of this project has been to demonstrate that a smartphone-based system can meet industry standards. My research shows that by using body proportions and machine learning, an average accuracy of 95.59% can be reached for critical dimensions such as waist, hip and thigh. The secret to the trillion-dollar problem of online returns is keeping errors within a margin of more or less than 2 centimeters.

The greatest challenge is in the technical realities of moving from a static 3D model to a dynamic “digital twin." The enhanced PBD framework is theoretically efficient enough to run in real-time on normal devices, but implementing the complex overlays and wrinkles of fabric in the open-source Godot Engine was a major technical bottleneck within the available time.

What’s Next? The application may not be fully "live," but I have defined a validated roadmap. The pipeline is ready for the next phase, which uses AI like Pix2Pix to help the system “see through” loose clothing and estimate body shape. The pipeline uses MakeHuman for anatomical realism and Mixamo for rigging.

This project shows that the fashion fit crisis has a promising open-source solution – it just needs a little more time in the fitting room of technical development.