TryOffDiff

TryOffDiff is a diffusion model-based high-fidelity clothing reconstruction technique used to generate standardized clothing images from a single photo of a wearing individual. This technology differs from traditional virtual try-on in that it is designed to extract canonical images of garments, which presents unique challenges in capturing garment shapes, textures and complex patterns. TryOffDiff ensures high fidelity and detail preservation by using Stable Diffusion and SigLIP-based visual conditions. Experiments on this technique on the VITON-HD dataset show that its method outperforms baseline methods based on pose transfer and virtual try-on, and requires fewer pre- and post-processing steps. TryOffDiff not only improves the quality of e-commerce product images, but also advances the evaluation of generative models and inspires future work on high-fidelity reconstruction.

The target audience includes e-commerce platforms, clothing retailers, fashion designers, and researchers in the field of image processing. TryOffDiff can help them improve product display effects, optimize customer experience, and achieve more accurate clothing image analysis in design and research through high-fidelity clothing image reconstruction technology.