Enhancing Virtual Try-on with Vision Transformers and Multi-Part Segmentation

Virtual apparel try-on technology aims to provide users with a realistic visualization of how clothes fit and appear, transforming the online shopping experience. This project introduces a modular approach leveraging deep learning techniques, including Vision Transformers, pose estimation, and human parsing, to achieve precise garment fitting and alignment. Segmentation masks and keypoint-based pose annotations enable accurate body part localization, while cross-attention mechanisms ensure seamless integration of clothing with user images. By addressing challenges like texture preservation, pose adaptability, and part-specific segmentation, the system produces high-resolution, photorealistic outputs. This framework enhances accessibility for e-commerce platforms and moves virtual try-on solutions closer to replacing traditional fitting rooms.