Paper · arXiv 2411.18350
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
Riza Velioglu, Petra Bevandic, Robin Chan, Barbara Hammer
TryOffDiff, an adaptation of Stable Diffusion for generating standardized garment images from dressed individuals, outperforms pose transfer and virtual try-on methods using SigLIP-based visual conditioning and the DISTS metric for evaluation.
Abstract
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/