Inverse Virtual Try-On: Generating Multi-Category Product-Style Images
  from Clothed Individuals
Authors: name: Davide Lobba, name: Fulvio Sanguigni, name: Bin Ren, name: Marcella Cornia, name: Rita Cucchiara, name: Nicu Sebe

While virtual try-on (VTON) systems aim to render a garment onto a target
person image, this paper tackles the novel task of virtual try-off (VTOFF),
which addresses the inverse problem: generating standardized product images of
garments from real-world photos of clothed individuals. Unlike VTON, which must
resolve diverse pose and style variations, VTOFF benefits from a consistent and
well-defined output format -- typically a flat, lay-down-style representation
of the garment -- making it a promising tool for data generation and dataset
enhancement. However, existing VTOFF approaches face two major limitations: (i)
difficulty in disentangling garment features from occlusions and complex poses,
often leading to visual artifacts, and (ii) restricted applicability to
single-category garments (e.g., upper-body clothes only), limiting
generalization. To address these challenges, we present Text-Enhanced
MUlti-category Virtual Try-Off (TEMU-VTOFF), a novel architecture featuring a
dual DiT-based backbone with a modified multimodal attention mechanism for
robust garment feature extraction. Our architecture is designed to receive
garment information from multiple modalities like images, text, and masks to
work in a multi-category setting. Finally, we propose an additional alignment
module to further refine the generated visual details. Experiments on VITON-HD
and Dress Code datasets show that TEMU-VTOFF sets a new state-of-the-art on the
VTOFF task, significantly improving both visual quality and fidelity to the
target garments.