This paper introduces Virtual Try-Off (VTOFF), a novel task focused on generating standardized garment images from single photos of clothed individuals. Unlike traditional Virtual Try-On (VTON), which digitally dresses models, VTOFF aims to extract a canonical garment image, posing unique challenges in capturing garment shape, texture, and intricate patterns. This well-defined target makes VTOFF particularly effective for evaluating reconstruction fidelity in generative models. We present TryOffDiff, a model that adapts Stable Diffusion with SigLIP-based visual conditioning to ensure high fidelity and detail retention. Experiments on a modified VITON-HD dataset show that our approach outperforms baseline methods based on pose transfer and virtual try-on with fewer pre- and post-processing steps. Our analysis reveals that traditional image generation metrics inadequately assess reconstruction quality, prompting us to rely on DISTS for more accurate evaluation. Our results highlight the potential of VTOFF to enhance product imagery in e-commerce applications, advance generative model evaluation, and inspire future work on high-fidelity reconstruction. Demo, code, and models are available at: https://rizavelioglu.github.io/tryoffdiff/

本文提出了虚拟试穿（VTOFF），该任务专注于从穿衣个体的单张照片生成标准化的服装图像。与传统的虚拟试穿不同，VTOFF旨在提取典型的服装图像，突出服装形状、纹理和复杂图案的捕捉难度。研究显示，该方法能够有效提高电商产品图像的质量，并推动生成模型评估的进步。

TryOffDiff：通过高保真服装重建实现虚拟试穿