AI-Driven Virtual Model Generation for Fashion Catalog Creation

Virtual try-on systems have drawn a lot of interest because of the potential uses they have in e-commerce and fashion. Existing techniques, however, have trouble supporting arbitrary positions, retaining individual identity, and preserving clothing details. In this project, we propose a novel Detail-Oriented Virtual Try-On Network, designed to generate high-quality try-on images under arbitrary human poses. Three main components comprise our approach: a Try-On Synthesis Module that creates the final image while maintaining the person’s identity and intricate clothing details; a Semantic Prediction Module that gradually predicts a semantic map of the person, ensuring accurate shape and pose alignment; and a Spatial Alignment Module that warps the target clothing to fit the person’s body and pose seamlessly. We present a multi-scale dilated convolution U-Net architecture to significantly improve image quality, capturing both fine-grained local details and larger contextual information. Our Detail-Oriented Virtual Try-On Network system beats state-of-the-art techniques, providing realistic and detailed virtual try-on experiences with superior pose flexibility and texture preservation, as shown by extensive testing on benchmark datasets.