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Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles
Image-based virtual try-on systems, which fit new garments onto human portraits, are gaining research attention. An ideal pipeline should preserve the static features of clothes(like textures and logos)while also generating dynamic elements(e. g. shadows, folds)that adapt to the model's pose and environment. Previous works fail specifically in generating dynamic features, as they preserve the warped in-shop clothes trivially with predicted an alpha mask by composition. To break the dilemma of over-preserving and textures losses, we propose a novel diffusion-based Product-level virtual try-on pipeline,\ie PLTON, which can preserve the fine details of logos and embroideries while producing realistic clothes shading and wrinkles. The main insights are in three folds:1)Adaptive Dynamic Rendering:We take a pre-trained diffusion model as a generative prior and tame it with image features, training a dynamic extractor from scratch to generate dynamic tokens that preserve high-fidelity semantic information.
