TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction
  using Diffusion Models
Authors: name: Riza Velioglu, name: Petra Bevandic, name: Robin Chan, name: Barbara Hammer

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/