Technical Implementation and Business Value of Virtual Dress Fitting
Poify's AI model fitting uses Generative Adversarial Network (GAN) technology to build a data model containing 2,000+ human postures. After the user uploads a picture of a flat garment, the system first recognizes the points of the garment features such as neckline and cuffs through semantic segmentation, and then maps the garment onto the virtual model's body through a 3D reconstruction algorithm, and finally outputs a multi-angle display effect.
This function solves two major pain points in the e-commerce clothing industry: first, it eliminates the high-cost aspects such as actual modeling appointments and venue rentals, and the cost of a single generation tends to be close to zero; and second, it breaks through time and space constraints and can realize 24-hour uninterrupted content production. Test data show that the average time for the system to process an ordinary T-shirt is 17 seconds, and it supports the generation of three standard display perspectives: front, side and back.
At present, the function has an accuracy of 95% for solid color or simple patterned clothing, but manual fine-tuning is required for multi-layer designs or complex patterns. The system provides basic model templates for different body types such as Asian, European and American, but does not support the customization of model appearance characteristics. The generated fitting pictures can be directly applied to the product detail page, which has been tested to improve the conversion rate of the product page by about 30%.
This answer comes from the articlePoify: an AI-powered e-commerce image generation and editing toolThe
