Free Try-On: Virtual Try-On without Garment-Agnostic Images and Warped Garments

Virtual try-on focuses on transferring garment images onto target human images. Despite advancements in diffusion-based try-on models, existing methods face limitations, including the loss of human appearance details caused by garment-agnostic images and artifacts from warped garment images. To address these issues, we propose a training method based on pseudo-labeled data, which eliminates the need for garment-agnostic images by leveraging a pretrained try-on model to generate additional training data. Furthermore, we introduce a bidirectional interaction dual UNet and a Reference Fusion mechanism, enabling the generation of high-quality try-on images without using warped garments. Our model also supports try-on image generation from garment text descriptions. Additionally, we enhance Stable Diffusion’s Variational Autoencoder (VAE) with two extra encoders, significantly improving the quality of low-resolution try-on image generation. Experiments on the VITON-HD and DressCode datasets demonstrate superior qualitative and quantitative performance compared to existing methods. Code is available at https://github.com/heiheizwplus/Free-tryon.